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REVIEW 4 major objections 4 minor 107 references

Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A staged vehicle-in-virtual-environment pipeline with a barrier-function safety layer aims to make bicyclist crash scenarios testable before road deployment.

desk verdict Honest engineering progress report; the VVE pipeline is genuinely useful, but the paper's headline safety guarantee from the HOCBF layer is invalidated by input saturation, and the DRL system was never VVE-tested. read the letter →

arxiv 2509.00624 v1 pith:F342AZR2 submitted 2025-08-30 cs.RO cs.SYeess.SY

Haochong Chen , Xincheng Cao , Bilin Aksun-Guvenc , Levent Guvenc This is my paper
classification cs.ROcs.SYeess.SY
keywords autonomousdrivingvulnerableroadusersafetybicyclistcontrolbarrierfunctionsdeepreinforcementlearningvehicle-in-virtual-environmenthardware-in-the-loopdelay-tolerant
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This project report argues that vulnerable-road-user safety functions for autonomous vehicles can be developed and validated without exposing the public to unproven software. Its route is a staged pipeline—pure simulation, hardware-in-the-loop, and a real vehicle driven inside a virtual traffic world—with a low-level controller that overrides unsafe high-level decisions through barrier-function constraints. The central object is the Vehicle-in-Virtual-Environment (VVE) setup, which synchronizes a real car's motion with a virtual twin so that dangerous bicyclist scenarios can be replayed safely and cheaply. If this works, developers could test emergency braking, lane-change, and overtaking maneuvers against the five most common fatal bicyclist crash types before any real road exposure.

What carries the argument

The load-bearing mechanism is the VVE synchronization loop: the real vehicle's position and heading are transformed into a virtual traffic world, while virtual sensor data—including the virtual bicyclist's motion—feeds back to the real onboard controller, so the real car reacts to a virtual traffic scenario in real time. Beneath that, the safety guarantee is carried by the HOCLF-HOCBF-QP layer, a quadratic program whose high-order control barrier function constraints keep the vehicle out of obstacle danger zones (a 2 m margin) regardless of what the high-level DRL agent commands. The CDOB delay-tolerant path tracker is the third piece, recasting unknown time delays as disturbances to be esti

What would settle it

Run the same FARS 230 overtaking scenario on a vehicle whose perception pipeline adds realistic position errors (for example, 1 to 3 meters of noise to the detected bicyclist position) while the barrier-function controller uses those noisy states; if the minimum distance falls below the 2 m margin or a collision occurs, the claimed safety guarantee does not transfer to real perception.

Watch

Extended reading notes

Core claim

The paper's central claim is that combining a hierarchical decision-and-control stack with a VVE testing pipeline is a viable way to develop and evaluate autonomous driving functions aimed at bicyclist safety. The stack pairs a high-level deep-reinforcement-learning agent that chooses lane-level maneuvers with a low-level control-Lyapunov/control-barrier-function quadratic program (CLF-CBF-QP) that enforces hard safety constraints, so even a wrong high-level command cannot drive the vehicle into a bicyclist. The testing pipeline moves from model-in-the-loop, through hardware-in-the-loop, into VVE, where a real vehicle's position and heading are mapped into a virtual traffic world and virtual

Load-bearing premise

The safety margins reported in the simulations come from exact simulator states—obstacle position, ego position, and heading are assumed known—so the guarantee transfers to a real vehicle only if real perception errors stay below the roughly 2 m margin.

Editorial extensions

If this is right

  • If the pipeline works as claimed, the five FARS bicyclist crash types can be tested safely and repeatedly in a virtual environment without public road exposure.
  • With the low-level barrier-function layer in the loop, an erroneous high-level maneuver such as merging into a bicyclist is corrected locally by steering and braking, so safety does not depend on the reinforcement-learning policy being perfect.
  • The staged MIL-to-HIL-to-VVE sequence can expose hardware-related issues such as signal delays and unrealistic input spikes before deployment, making public road tests a final confirmation rather than a first exposure.
  • The delay-tolerant CDOB path tracker maintains tracking accuracy under unknown time delays, which broadens the pipeline's applicability to connected and communication-delayed driving.
  • The framework is modular: the high-level DRL agent can be swapped for other algorithms without changing the low-level safety layer, allowing comparisons of decision-making methods on the same safety-critical core.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If real perception errors exceed the roughly 2 m safety margin used in the simulations, the barrier-function guarantee does not transfer to a vehicle relying on noisy camera and lidar data; injecting realistic perception noise into the VVE loop would be a direct test of this gap.
  • The report's own decision not to VVE-test the DDQN emergency-braking agent because it lacks hard-coded safety rules suggests that the authors treat the barrier-function layer as a prerequisite for real-vehicle VVE testing; adding a CBF safety filter to the learned agent and running it in VVE is a natural extension.
  • The VVE framework's multi-actor capability points toward testing not just the ego vehicle but the interaction between real pedestrians or bicyclists and the virtual environment, which the paper lists as future work.
  • The value of the pipeline depends on how faithfully the virtual world recreates both the crash geometry and the sensor conditions; if the virtual scenarios omit real lighting, occlusion, or sensor noise, the validation is likely optimistic.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This manuscript, formatted as a second-year project report, proposes three contributions for improving vulnerable road user (VRU) safety in connected and automated driving: (1) a CDOB-based delay-tolerant path-tracking controller, (2) a hierarchical collision-avoidance framework combining a high-level DRL agent (DQN/DDQN) with a low-level HOCLF-HOCBF-QP controller, and (3) a Vehicle-in-Virtual-Environment (VVE) testing pipeline. The authors recreate five FARS bicyclist crash scenarios in CARLA and report simulation, MIL, HIL, and partial VVE experiments. The stated aim is to use the VVE pipeline together with the hierarchical controller to develop and evaluate bicyclist-safety functions that can prevent FARS-type crashes.

Significance. If the central safety claim were fully supported, the paper would offer a useful staged testing pipeline and a safety-filter architecture for VRU collision avoidance. The VVE concept itself is previously published by the same group, and the main new value resides in applying it to bicyclist scenarios. The paper shows several promising components: real-time QP solve times (~0.66 ms), a modular controller design, and a clear argument for why a CBF layer can rescue unsafe high-level decisions. However, the reported evidence does not yet support the strongest claims: the hard safety guarantee is invalidated by input saturation and by an incorrect HOCBF derivative for moving obstacles, and the VVE pipeline is not tested with the proposed hierarchical controller or with bicyclist scenarios. The paper also provides no code, no randomized trials, and no external benchmarks.

major comments (4)
  1. [Sec. 4.2.2.3, Eq. (4.37), Table 4.3] The CBF safety argument requires the applied input to satisfy the HOCBF constraint. The QP in Eq. (4.37) is formulated without input limits, and Section 4.2.2.3 states that the unicycle QP is solved without input constraints and the computed input is saturated afterward. The vehicle-dynamic QP in Eq. (4.37) likewise contains no steering bounds, even though Table 4.3 defines ±0.7 rad limits. If saturation is active, the applied input does not satisfy Eq. (4.36), forward invariance of the safe set is lost, and the claimed ≥2 m minimum distance in Fig. 4.19 is not guaranteed. The paper does not verify that saturation was inactive in the reported runs. This is an internal technical gap, not merely a deployment concern.
  2. [Sec. 4.2.2.4, Eqs. (4.32)-(4.36)] The HOCBF derivative is computed as if the obstacle coordinates (x_o, y_o) are constant. For the dynamic-obstacle simulations and the FARS230 bicyclist scenario, the obstacle is moving, so the total derivative of h contains the terms -2(x-x_o) xdot_o - 2(y-y_o) ydot_o. These obstacle-velocity terms are omitted in Eqs. (4.33)-(4.35). Consequently, the constraint in Eq. (4.36) does not ensure h ≥ 0 over time for a moving obstacle. Recomputing the obstacle list at each low-level step (Algorithm 4.2, line 10) only enforces the distance condition at sample instants. The min-distance plot in Fig. 4.19 is therefore scenario-specific evidence, not the claimed safety guarantee, and the analogous claim for the moving bicyclist in FARS230 is unsupported.
  3. [Sec. 4.3.2, Sec. 4.4] The simulation-based evaluation of the hierarchical controller is a single deterministic run. There are no error bars, random seeds, perturbation tests, or sensitivity analyses; Section 4.4 explicitly states that 'more detailed sensitivity analyses and ablation studies' are future work. The FARS230 demonstration with the HOCLF-HOCBF-QP controller is presented only as trajectory snapshots (Figs. 4.20-4.21), without a minimum-distance metric, time-to-collision metric, or comparison to a baseline. The DDQN agent is assessed against the reward function it was trained to maximize, in the same training environment. These limitations undermine the 'rigorous evaluation' claimed in contribution (3).
  4. [Sec. 5.3.3, Sec. 5.4] The proposed VVE pipeline is not demonstrated for the proposed hierarchical controller or for the five FARS bicyclist scenarios. Section 5.3.3 states that the DDQN emergency-brake algorithm was not VVE-tested because it lacks hard-coded safety rules, and Section 5.4 states that a full test of the DRL-based collision avoidance algorithm is 'still in progress.' The only VVE result uses a pure-pursuit lane-change controller. Thus the paper's central claim of developing and evaluating bicyclist-safety functions via VVE is not supported by the reported experiments. Additionally, Chapter 2's perception and trajectory-prediction components are described but never implemented or integrated; all safety evaluations use ground-truth obstacle states.
minor comments (4)
  1. [Throughout] There are numerous typos and inconsistent figure references: 'denates' on p.25, 'radiii' on p.31, and 'TTZ' is used where 'TTC' is standard. The text at p.50 says 'Figure 4.18 illustrates the real time distance' but the figure is labeled 4.19; other cross-references are similarly shifted.
  2. [Eq. (4.14)] The HOCLF condition contains an undefined term S(h(x)). Please define it or remove it; the standard HOCLF condition does not include such a term.
  3. [Chapter 3] The CDOB delay-tolerant controller is presented as a contribution, but it is not integrated with the hierarchical collision-avoidance framework or with the VVE tests. The relationship between Chapter 3 and the rest of the pipeline should be clarified.
  4. [Chapter 5 / References [46,104]] The VVE method is described as 'novel' in this paper, but the text itself cites the authors' previous papers [46] and [104] as the source of VVE. Please state explicitly what is new in the present VVE pipeline relative to those prior works.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; self-citations and self-defined metrics present but not load-bearing.

full rationale

The report's core derivations are standard and self-contained: the CLF-CBF-QP/HOCBF formulation follows the established theory (Ames et al., Xiao & Belta), the CDOB design is a textbook disturbance-observer variant, and the DRL agents are trained and tested in simulation without claiming external benchmark generalization. The VVE method and the DDQN emergency-brake algorithm are drawn from the authors' own prior publications ([46],[104],[106]), and Chapter 5 explicitly concedes that the DDQN agent was not VVE-tested (Section 5.3.3), so the VVE demonstration is narrower than the abstract implies. The reported 2 m safety margin is consistent with the HOCBF constraint (Eqs. 4.32-4.36), but the paper never specifies that r_o equals 2 m, so it cannot be shown by construction that the result is merely the constraint restated; at most it is a verification of a designer-chosen invariant. The input-saturation issue (Section 4.2.2.3) and the use of simulator ground truth are correctness/transfer limitations, not circularity. Overall, the central claims retain independent technical content despite multiple self-citations.

Assumptions & free parameters 5 free parameters · 7 assumptions · 0 invented entities

The report rests on standard control-theory results and on the authors' own previously published vehicle and VVE models. It introduces no new fundamental constants or physical entities, but it assumes perfect perception, simplified vehicle dynamics, and that simulator scenarios are realistic. Several controller gains and reward weights are hand-chosen and unreported, which limits independent reproduction.

free parameters (5)
  • HOCLF/HOCBF class-kappa gains and relaxation penalty q = not reported (only 'positive constant gains')
    Chosen by hand to make the QP stable; no tuning rule or values are given (Section 4.2.2.4).
  • DRL reward weights and shaped reward terms = +25/-300/-1 for unicycle; +50/-100/-0.5 plus progress coefficient v for dynamic model
    Hand-designed rewards are the training objective; the agent's success is measured against constants the authors chose (Sections 4.3.1 and 4.3.2).
  • DQN/DDQN hyperparameters = learning rate 0.001, batch size 64, replay buffer 100,000, epsilon decay 1.0 to 0.05 over 200,000 steps
    Selected without sensitivity analysis; different hyperparameters would change the training curves and policy performance (Table 4.4).
  • Safe distance threshold and obstacle radius r_o = 2 m for the safety threshold; r_o not reported
    The collision-avoidance result is interpreted against this ad hoc margin (Section 4.3.2, Figure 4.19).
  • CDOB/PID gains and Q-filter bandwidth = not reported
    Speed-scheduled PID gains are selected inside an admissible parameter-space region, but the chosen values and Q-filter design are not given (Section 3.4.3).
assumptions (7)
  • standard math CLF/CBF/HOCBF theory from Ames and Xiao guarantees stability and safety when the QP constraints are feasible.
    Invoked in Section 4.2.2 with citations [47-49], [60], [85]; the report does not verify the class-kappa functions for the actual vehicle models.
  • domain assumption Perfect state and obstacle position knowledge in simulation.
    HOCBF constraints in Section 4.3.2 use ground-truth coordinates; perception uncertainty and latency are not modeled.
  • domain assumption A linear single-track model with constant speed and linear tire stiffness adequately represents real vehicle dynamics.
    Used in Section 4.2.1.2 and Table 4.1; the report itself acknowledges in Section 4.4 that the model is simplified compared with full vehicle systems.
  • domain assumption CARLA / Unreal Engine and the VVE frame transformation faithfully represent real traffic and real vehicle motion.
    Section 5.2.3 describes the synchronization, but no validation against real crash or trajectory data is provided.
  • domain assumption Reference path curvature is perfectly known for the CDOB feedforward.
    Section 3.4.2 states 'this structure works because the curvature of the reference path is known.'
  • ad hoc to paper FARS crash scenario recreations in CARLA are realistic enough to serve as safety evaluation cases.
    Chapter 1 presents five scenarios with video links only; no validation against FARS kinematics or real incident data is reported.
  • ad hoc to paper DRL training convergence implies a safe policy when combined with CBF filtering.
    Sections 4.3.1 and 4.3.2 use reward curves as evidence of success; no formal safety guarantee is proved for the learned policy.

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Cite this review

Pith. "Pith review of Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety." pith.science (2026). https://pith.science/paper/F342AZR2

@misc{pith2026250900624,
  author       = {Pith},
  title        = {Pith review of: Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F342AZR2}},
  note         = {Machine review of arXiv:2509.00624}
}
read the original abstract

Extensive research has already been conducted in the autonomous driving field to help vehicles navigate safely and efficiently. At the same time, plenty of current research on vulnerable road user (VRU) safety is performed which largely concentrates on perception, localization, or trajectory prediction of VRUs. However, existing research still exhibits several gaps, including the lack of a unified planning and collision avoidance system for autonomous vehicles, limited investigation into delay tolerant control strategies, and the absence of an efficient and standardized testing methodology. Ensuring VRU safety remains one of the most pressing challenges in autonomous driving, particularly in dynamic and unpredictable environments. In this two year project, we focused on applying the Vehicle in Virtual Environment (VVE) method to develop, evaluate, and demonstrate safety functions for Vulnerable Road Users (VRUs) using automated steering and braking of ADS. In this current second year project report, our primary focus was on enhancing the previous year results while also considering bicyclist safety.

Figures

Figures reproduced from arXiv: 2509.00624 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3.1
Figure 3.1. Reference path optimization: (a) optimized path; (b) path curvature of the optimized path [PITH_FULL_IMAGE:figures/full_fig_p012_3_1.png] view at source ↗
Figure 3.2
Figure 3.2. Path-tracking scenario [33] 3. 4 Delay-Tolerant Path-Tracking Control Design 3.4.1 Communication Disturbance Observer (CDOB) This sub-section presents a general overview of the communication disturbance observer (CDOB), which is an approach inspired by the disturbance observer (DOB). Please see references [29], [34], [35] for more details on the CDOB. Given a time-delayed input-output system as shown in [PITH_FULL_… view at source ↗
Figures from the paper (14 more)
Figure 3.3
Figure 3.3. Figure 3.3: Sample input-output system with time delay Once it has been established that the unknown time delay can be remodeled as a disturbance, the concept of DOB can be used to estimate and compensate for the time delay [PITH_FULL_IMAGE:figures/full_fig_p015_3_3.png]
Figure 3.4
Figure 3.4. Figure 3.4: Standard CDOB block diagram 3.4.2 Modification for Path Curvature Rejection As mentioned in Section 3.3, the vehicle model used for path-tracking is derived such that reference path curvature enters the model as an external disturbance. Denoting the path curvature di…
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p016_3.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p017_3.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p018_3.png]
Figure 3.10
Figure 3.10. Figure 3.10: It can be observed that the vehicle is able to track the reference [PITH_FULL_IMAGE:figures/full_fig_p019_3_10.png]
Figure 3.10
Figure 3.10. Figure 3.10: Forward motion modified CDOB + PID simulation results 3.5.2 Hardware-in-the-Loop (HIL) Experiment In addition to the simulation study, hardware-in-the-loop (HIL) experiments are also performed to further demonstrate the suitability of this proposed control scheme fo…
Figure 3.11
Figure 3.11. Figure 3.11: It [PITH_FULL_IMAGE:figures/full_fig_p020_3_11.png]
Figure 3.11
Figure 3.11. Figure 3.11: Forward motion modified CDOB + PID HIL results [PITH_FULL_IMAGE:figures/full_fig_p021_3_11.png]
Figure 4
Figure 4. Figure 4: illustrates an example of the traffic [PITH_FULL_IMAGE:figures/full_fig_p036_4.png]
Figure 4
Figure 4. Figure 4: illustrates a representative scenario where the high [PITH_FULL_IMAGE:figures/full_fig_p051_4.png]
Figure 4
Figure 4. Figure 4: demonstrates the behavior of the ego vehicle when the HOCLF [PITH_FULL_IMAGE:figures/full_fig_p052_4.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p072_5.png]
Figure 5
Figure 5. Figure 5: (b), we replicated [PITH_FULL_IMAGE:figures/full_fig_p073_5.png]

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Works this paper leans on

107 extracted references · 52 canonical work pages

  1. [1]

    Geneva: World Health Organization, 2015

    World Health Organization, Global status report on road safety 2015. Geneva: World Health Organization, 2015. Accessed: Oct. 24, 2023. [Online]. Available: https://iris.who.int/handle/10665/189242

    2015

  2. [2]

    Investigating the Role of Roadway Environment in Driving Errors: An on Road Study,

    P. M. Salmon, K. L. Young, and M. G. Lenné, “Investigating the Role of Roadway Environment in Driving Errors: An on Road Study,” Proc. Hum. Factors Ergon. Soc. Annu. Meet., vol. 55, no. 1, pp. 1879–1883, Sep. 2011, doi: 10.1177/1071181311551391

    work page doi:10.1177/1071181311551391 2011

  3. [3]

    Relationship between Infrastructure, Driver Error, and Critical Incidents,

    A. Medina, S. Lee, W. Wierwille, and R. Hanowski, “Relationship between Infrastructure, Driver Error, and Critical Incidents,” Proc. Hum. Factors Ergon. Soc. Annu. Meet., vol. 48, pp. 2075–2079, Sep. 2004, doi: 10.1177/154193120404801661

    work page doi:10.1177/154193120404801661

  4. [4]

    State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey,

    P. Ghorai, A. Eskandarian, Y .- K. Kim, and G. Mehr, “State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 10, pp. 16983–17002, Oct. 2022, doi: 10.1109/TITS.2022.3160932

    arXiv 2022

  5. [5]

    Review of tracking and object detection systems for advanced driver assistance and autonomous driving applications with focus on vulnerable road users sensing,

    P. Markiewicz, M. Długosz, and P. Skruch, “Review of tracking and object detection systems for advanced driver assistance and autonomous driving applications with focus on vulnerable road users sensing,” in Trends in Advanced Intelligent Control, Optimiza tion and Automation, Springer, Cham, 2017, pp. 224–237. doi: 10.1007/978-3-319-60699-6_22

    work page doi:10.1007/978-3-319-60699-6_22 2017

  6. [6]

    Predicting Pedestrian Crossing Intention With Feature Fusion and Spatio- Temporal Attention,

    D. Yang, H. Zhang, E. Yurtsever, K. A. Redmill, and Ü. Özgüner, “Predicting Pedestrian Crossing Intention With Feature Fusion and Spatio- Temporal Attention,” IEEE Trans. Intell. Veh., vol. 7, no. 2, pp. 221–230, Jun. 2022, doi: 10.1109/TIV .2022.3162719

    arXiv 2022

  7. [7]

    Faraway- Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion,

    H. Zhang, D. Yang, E. Yurtsever, K. A. Redmill, and Ü. Özgüner, “Faraway- Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Sep. 2021, pp. 2646–

    2021

  8. [8]

    Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users,

    Y . Guo, Y . Liu, B. Wang, P. Huang, H. Xu, and Z. Bai, “Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users,” Accid. Anal. Prev., vol. 203, p. 107610, Aug. 2024, doi: 10.1016/j.aap.2024.107610

    arXiv 2024

Show all 107 references
  1. [9]

    Rich feature hierarchies for accurate object detection and semantic segmentation,

    R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Oct. 22, 2014, arXiv: arXiv:1311.2524. doi: 10.48550/arXiv.1311.2524

    2014 doi arXiv

  2. [10]

    Fast R -CNN,

    R. Girshick, “Fast R -CNN,” Sep. 27, 2015, arXiv: arXiv:1504.08083. doi: 10.48550/arXiv.1504.08083

    2015 doi arXiv

  3. [11]

    Faster R -CNN: Towards Real -Time Object Detection with Region Proposal Networks,

    S. Ren, K. He, R. Girshick, and J. Sun, “Faster R -CNN: Towards Real -Time Object Detection with Region Proposal Networks,” Jan. 06, 2016, arXiv: arXiv:1506.01497. doi: 10.48550/arXiv.1506.01497

    2016 doi arXiv

  4. [12]

    Sparse R -CNN: End-to-End Object Detection with Learnable Proposals,

    P. Sun et al., “Sparse R -CNN: End-to-End Object Detection with Learnable Proposals,” Apr. 26, 2021, arXiv: arXiv:2011.12450. doi: 10.48550/arXiv.2011.12450

    2021 doi arXiv

  5. [13]

    You Only Look Once: Unified, Real- Time Object Detection,

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real- Time Object Detection,” May 09, 2016, arXiv: arXiv:1506.02640. doi: 10.48550/arXiv.1506.02640

    2016 doi arXiv

  6. [14]

    YOLOv8-CB: Dense Pedestrian Detection Algorithm Based on In- Vehicle Camera,

    Q. Liu, H. Ye, S. Wang, and Z. Xu, “YOLOv8-CB: Dense Pedestrian Detection Algorithm Based on In- Vehicle Camera,” Electronics, vol. 13, no. 1, Art. no. 1, Jan. 2024, doi: 10.3390/electronics13010236

    2024 doi

  7. [15]

    Laser Scanners for Driver-Assistance Systems in Intelligent 78 Vehicles [Applications of Control],

    S. Velupillai and L. Guvenc, “Laser Scanners for Driver-Assistance Systems in Intelligent 78 Vehicles [Applications of Control],” IEEE Control Syst. Mag., vol. 29, no. 2, pp. 17–19, Apr. 2009, doi: 10.1109/MSP.2008.931716

    2009

  8. [16]

    Lidar Data Analysis for Time to Headway Determination in the DriveSafe Project Field Tests,

    İ. Altay, B. Aksun-Guvenc, and L. G uvenc, “Lidar Data Analysis for Time to Headway Determination in the DriveSafe Project Field Tests,” Int. J. Veh. Technol., vol. 2013, no. 1, p. 749896, 2013, doi: 10.1155/2013/749896

    2013 doi

  9. [17]

    Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication,

    S. Y . Gelbal, B. Aksun- Guvenc, and L. Guvenc, “Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication,” Electronics, vol. 13, no. 2, Art. no. 2, Jan. 2024, doi: 10.3390/electronics13020331

    2024 doi

  10. [18]

    PointPillars: Fast Encoders for Object Detection from Point Clouds,

    A. H. Lang, S. V ora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “PointPillars: Fast Encoders for Object Detection from Point Clouds,” May 07, 2019, arXiv: arXiv:1812.05784. doi: 10.48550/arXiv.1812.05784

    2019 doi arXiv

  11. [19]

    CIA-SSD: Confident IoU -Aware Single-Stage Object Detector From Point Cloud,

    W. Zheng, W. Tang, S. Chen, L. Jiang, and C.- W. Fu, “CIA-SSD: Confident IoU -Aware Single-Stage Object Detector From Point Cloud,” Dec. 05, 2020, arXiv: arXiv:2012.03015. doi: 10.48550/arXiv.2012.03015

    2020 doi arXiv

  12. [21]

    V2X Communication between Connected and Automated Vehicles (CA Vs) and Unmanned Aerial Vehicles (UA Vs),

    O. Kavas-Torris, S. Y . Gelbal, M. R. Cantas, B. Aksun- Guvenc, and L. Guvenc, “V2X Communication between Connected and Automated Vehicles (CA Vs) and Unmanned Aerial Vehicles (UA Vs),” Sensors, vol. 22, no. 22, Art. no. 22, Jan. 2022, doi: 10.3390/s22228941

    2022 doi

  13. [22]

    Visual Object Detection Based LiDAR Point Cloud Classification,

    S. Muhammad and G.- W. Kim, “Visual Object Detection Based LiDAR Point Cloud Classification,” in 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Feb. 2020, pp. 438–440. doi: 10.1109/BigComp48618.2020.00-32

    2020

  14. [23]

    3D Object Detection Based on LiDAR Data,

    R. Sahba, A. Sahba, M. Jamshidi, and P. Rad, “3D Object Detection Based on LiDAR Data,” in 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 2019, pp. 0511–0514. doi: 10.1109/UEMCON47517.2019.8993088

    2019

  15. [24]

    A LiDAR–Camera Fusion 3D Object Detection Algorithm,

    L. Liu, J. He, K. Ren, Z. Xiao, and Y . Hou, “A LiDAR–Camera Fusion 3D Object Detection Algorithm,” Information, vol. 13, no. 4, Art. no. 4, Apr. 2022, doi: 10.3390/info13040169

    2022 doi

  16. [25]

    LiDAR -Based Intensity- Aware Outdoor 3D Object Detection,

    A. Y . Naich and J. R. Carrión, “LiDAR -Based Intensity- Aware Outdoor 3D Object Detection,” Sensors, vol. 24, no. 9, Art. no. 9, Jan. 2024, doi: 10.3390/s24092942

    2024 doi

  17. [26]

    A Spatio -Temporal Graph Transformer Network for Multi -Pedestrain Trajectory Prediction | IEEE Conference Publication | IEEE Xplore

    “A Spatio -Temporal Graph Transformer Network for Multi -Pedestrain Trajectory Prediction | IEEE Conference Publication | IEEE Xplore.” Accessed: Aug. 29, 2024. [Online]. Available: https://ieeexplore -ieee-org.proxy.lib.ohio- state.edu/document/9927798

    2024

  18. [27]

    Jocher, A

    G. Jocher, A. Chaurasia, and J. Qiu, Ultralytics YOLO. (Jan. 2023). Python. Accessed: Aug. 26, 2024. [Online]. Available: https://github.com/ultralytics/ultralytics

    2023

  19. [28]

    CARLA - v20 carla_v20,

    “CARLA - v20 carla_v20,” Roboflow. Accessed: Aug. 26, 2024. [Online]. Available: https://universe.roboflow.com/alec-hantson-student-howest-be/carla-izloa/dataset/20

    2024

  20. [29]

    Guvenc, B

    L. Guvenc, B. Aksun-Guvenc, B. Demirel, M.T. Emirler, 2017, Control of Mechatronic Systems, the IET, London, ISBN: 978-1-78561-144-5

    2017

  21. [30]

    Disturbance Observer- Smith Predictor Compensation-Based Platoon Control with Estimation Deviation,

    J. Wang, G. Wu, B. Sun, F. Ma, B. Aksun-Guvenc, and L. Guvenc, “Disturbance Observer- Smith Predictor Compensation-Based Platoon Control with Estimation Deviation,” J. Adv. Transp., vol. 2022, no. 1, p. 9866794, 2022, doi: 10.1155/2022/9866794. 79

    2022 doi

  22. [31]

    Path Planning and Robust Path Tracking Control of an Automated Parallel Parking Maneuver,

    Cao, X. and Guvenc, L., “Path Planning and Robust Path Tracking Control of an Automated Parallel Parking Maneuver,” SAE Technical Paper 2024-01-2558, 2024, doi:10.4271/2024-01-2558

    2024 doi

  23. [32]

    Oscillatory Yaw Motion Control for Hydraulic Power Steering Articulated Vehicles Considering the Influence of Varying Bulk Modulus,

    Y . Gao, Y . Shen, T. Xu, W. Zhang, and L. Güvenç, “Oscillatory Yaw Motion Control for Hydraulic Power Steering Articulated Vehicles Considering the Influence of Varying Bulk Modulus,” IEEE Trans. Control Syst. Technol., vol. 27, no. 3, pp. 1284–1292, May 2019, doi: 10.1109/TC...

    2019

  24. [33]

    Guvenc, L., Aksun- Guvenc, B., Zhu, S., Gelbal, S.Y ., 2022, Autonomous Road Vehicle Path Planning and Tracking Control, Wiley / IEEE Press, Book Series on Control Systems Theory and Application, New York, ISBN: 978-1-119-74794-9

    2022

  25. [34]

    M. T. Emirler, B. Aksun-Guvenc, and L. Guvenc, Communication Disturbance Observer Approach to Control of Integral Plant with Time Delay. Ieee, 2013. Accessed: Jul. 26, 2025. [Online]. Available: https://hdl.handle.net/20.500.14517/6374

    2013

  26. [35]

    Communication Disturbance Observer,

    A. Shimada, “Communication Disturbance Observer,” in Disturbance Observer for Advanced Motion Control with MATLAB / Simulink, IEEE, 2023, pp. 141–149. doi: 10.1002/9781394178131.ch7

    2023 doi

  27. [36]

    Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following,

    H. Wang, S. Y . Gelbal, and L. Guvenc, “Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following,” IEEE Access, vol. 9, pp. 46874–46885, 2021, doi: 10.1109/ACCESS.2021.3066925

    2021

  28. [37]

    Development and Evaluation of Path and Speed Profile Planning and Tracking Control for an Autonomous Shuttle Using a Realistic, Virtual Simulation Environment,

    X. Li, S. Zhu, B. Aksun- Guvenc, and L. Guvenc, “Development and Evaluation of Path and Speed Profile Planning and Tracking Control for an Autonomous Shuttle Using a Realistic, Virtual Simulation Environment,” J. Intell. Robot. Syst., vol. 101, no. 2, p. 42, Feb. 2021, doi: 10...

    2021 doi

  29. [38]

    Trajectory Planning of Autonomous Vehicles Based on Parameterized Control Optimization in Dynamic on-Road Environments,

    S. Zhu and B. Aksun- Guvenc, “Trajectory Planning of Autonomous Vehicles Based on Parameterized Control Optimization in Dynamic on-Road Environments,” J. Intell. Robot. Syst., vol. 100, no. 3, pp. 1055–1067, Dec. 2020, doi: 10.1007/s10846-020-01215-y

    2020 doi

  30. [39]

    Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment,

    B. Wen, S. Gelbal, B. Aksun- Guvenc, and L. Guvenc, “Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment,” Apr. 2018. doi: 10.4271/2018-01-1182

    2018 doi

  31. [40]

    SmartShuttle: a unified, scalable and replicable approach to connected and automated driving in a smart city,

    S. Y . Gelbal, B. Aksun-Guvenc, and L. Guvenc, “SmartShuttle: a unified, scalable and replicable approach to connected and automated driving in a smart city,” in Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, in SCO...

    2017

  32. [41]

    Connected and Autonomous Vehicles,

    L. Guvenc, B. Aksun-Guvenc, and M. T. Emirler, “Connected and Autonomous Vehicles,” in Internet of Things and Data Analytics Handbook, John Wiley & Sons, Ltd, 2017, pp. 581–595. doi: 10.1002/9781119173601.ch35

    2017 doi

  33. [42]

    Socially Acceptable Collision Avoidance System for Vulnerable Road Users,

    M. T. Emirler, H. Wang, and B. Aksun-Guvenc, “Socially Acceptable Collision Avoidance System for Vulnerable Road Users,” IFAC -Pap., vol. 49, pp. 436–441, Dec. 2016, doi: 10.1016/j.ifacol.2016.07.073

    2016 doi

  34. [43]

    General Obstacle Avoidance Capability Assessment for Autonomous Vehicles,

    E. Lowe and L. Guvenc, “General Obstacle Avoidance Capability Assessment for Autonomous Vehicles,” Electronics, vol. 13, no. 24, Art. no. 24, Jan. 2024, doi: 10.3390/electronics13244901

    2024 doi

  35. [44]

    Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds,

    E. Lowe and L. Guvenc, “Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds,” Electronics, vol. 12, no. 23, Art. no. 23, Jan. 2023, doi: 10.3390/electronics12234765. 80

    2023 doi

  36. [46]

    Vehicle -in-Virtual- Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration,

    X. Cao, H. Chen, S. Y . Gelbal, B. Aksun- Guvenc, and L. Guvenc, “Vehicle -in-Virtual- Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration,” Sensors, vol. 23, no. 11, Art. no. 11, Jan. 2023, doi: 10.3390/s23115088

    2023 doi

  37. [47]

    Control barrier function based quadratic programs with application to adaptive cruise control,

    A. D. Ames, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs with application to adaptive cruise control,” in 53rd IEEE Conference on Decision and Control, Dec. 2014, pp. 6271–6278. doi: 10.1109/CDC.2014.7040372

    2014

  38. [48]

    Control Barrier Function Based Quadratic Programs for Safety Critical Systems,

    A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada, “Control Barrier Function Based Quadratic Programs for Safety Critical Systems,” IEEE Trans. Autom. Control, vol. 62, no. 8, pp. 3861–3876, Aug. 2017, doi: 10.1109/TAC.2016.2638961

    2017

  39. [49]

    Control Barrier Functions: Theory and Applications,

    A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control Barrier Functions: Theory and Applications,” in 2019 18th European Control Conference (ECC), Jun. 2019, pp. 3420–3431. doi: 10.23919/ECC.2019.8796030

    2019

  40. [50]

    Safety Barrier Certificates for Collisions -Free Multirobot Systems,

    L. Wang, A. D. Ames, and M. Egerstedt, “Safety Barrier Certificates for Collisions -Free Multirobot Systems,” IEEE Trans. Robot., vol. 33, no. 3, pp. 661–674, Jun. 2017, doi: 10.1109/TRO.2017.2659727

    2017

  41. [51]

    CLF-CBF Based Quadratic Programs for Safe Motion Control of Nonholonomic Mobile Robots in Presence of Moving Obstacles,

    M. Desai and A. Ghaffari, “CLF-CBF Based Quadratic Programs for Safe Motion Control of Nonholonomic Mobile Robots in Presence of Moving Obstacles,” in 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Jul. 2022, pp. 16–

    2022

  42. [52]

    Safe Autonomous Multi-vehicle Navigation Using Path Following Control and Spline-Based Barrier Functions,

    M. F. Reis, G. A. Andrade, and A. P. Aguiar, “Safe Autonomous Multi-vehicle Navigation Using Path Following Control and Spline-Based Barrier Functions,” in Robot 2023: Sixth Iberian Robotics Conference, L. Marques, C. Santos, J. L. Lima, D. Tardioli, and M. Ferre, Eds., Cham: ...

    2023 doi

  43. [53]

    doi: 10.1109/AIM52237.2022.9863392

    2022

  44. [54]

    Safety -Critical Path Planning of Autonomous Surface Vehicles Based on Rapidly-Exploring Random Tree Algorithm and High Order Control Barrier Functions,

    Y . Li et al., “Safety -Critical Path Planning of Autonomous Surface Vehicles Based on Rapidly-Exploring Random Tree Algorithm and High Order Control Barrier Functions,” in 2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE), Jul. 2023, pp...

    2023

  45. [55]

    Sampling-based Motion Planning via Control Barrier Functions,

    G. Yang, B. Vang, Z. Serlin, C. Belta, and R. Tron, “Sampling-based Motion Planning via Control Barrier Functions,” in Proceedings of the 2019 3rd International Conference on Automation, Control and Robots, Oct. 2019, pp. 22–29. doi: 10.1145/3365265.3365282

    2019

  46. [56]

    Potential Game- Based Decision -Making for Autonomous Driving,

    M. Liu, I. Kolmanovsky, H. E. Tseng, S. Huang, D. Filev, and A. Girard, “Potential Game- Based Decision -Making for Autonomous Driving,” Nov. 09, 2023, arXiv: arXiv:2201.06157. doi: 10.48550/arXiv.2201.06157

    2023 doi arXiv

  47. [57]

    Rule -Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change,

    S. He, J. Zeng, B. Zhang, and K. Sreenath, “Rule -Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change,” Mar. 23, 2021, arXiv: arXiv:2103.12382. doi: 10.48550/arXiv.2103.12382

    2021 doi arXiv

  48. [58]

    Autonomous Vehicle Lane -Change Control Based on Model Predictive Control with Control Barrier Function,

    X. Chen, X. Liu, and M. Zhang, “Autonomous Vehicle Lane -Change Control Based on Model Predictive Control with Control Barrier Function,” in 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS), May 2024, pp. 1267–1272. doi: 10.1109/DDCLS61622.2024.10606562

    2024

  49. [59]

    A Novel Online Safe Reinforcement Learning with Control Barrier Function Technique for Autonomous vehicles,

    F. Jabbari, R. Samsami, and M. M. Arefi, “A Novel Online Safe Reinforcement Learning with Control Barrier Function Technique for Autonomous vehicles,” in 2024 10th International Conference on Control, Instrumentation and Automation (ICCIA), Nov. 2024, pp. 1–6. doi: 10.1109/ICC...

    2024

  50. [60]

    High- Order Control Barrier Functions,

    W. Xiao and C. Belta, “High- Order Control Barrier Functions,” IEEE Trans. Autom. Control, vol. 67, no. 7, pp. 3655–3662, Jul. 2022, doi: 10.1109/TAC.2021.3105491

    2022

  51. [61]

    Safety- Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions,

    A. Thirugnanam, J. Zeng, and K. Sreenath, “Safety- Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions,” May 31, 2022, arXiv: arXiv:2109.12313. doi: 10.48550/arXiv.2109.12313

    2022 doi arXiv

  52. [62]

    Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method,

    H. Wang, A. Tota, B. Aksun- Guvenc, and L. Guvenc, “Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method,” Mechatronics, vol. 50, pp. 341–355, Apr. 2018, doi: 10.1016/j.mechatronics.2017.11.009

    2018 doi

  53. [63]

    High-Order Control Lyapunov–Barrier Functions for Real-Time Optimal Control of Constrained Non- Affine Systems,

    A. E. Chriat and C. Sun, “High-Order Control Lyapunov–Barrier Functions for Real-Time Optimal Control of Constrained Non- Affine Systems,” Mathematics, vol. 12, no. 24, Art. no. 24, Jan. 2024, doi: 10.3390/math12244015

    2024 doi

  54. [64]

    Lunar Rover Collaborated Path Planning with Artificial Potential Field -Based Heuristic on Deep Reinforcement Learning,

    S. Lu, R. Xu, Z. Li, B. Wang, and Z. Zhao, “Lunar Rover Collaborated Path Planning with Artificial Potential Field -Based Heuristic on Deep Reinforcement Learning,” Aerospace, vol. 11, no. 4, Art. no. 4, Apr. 2024, doi: 10.3390/aerospace11040253

    2024 doi

  55. [65]

    Development of a Collision Avoidance Algorithm Using Elastic Band Theory,

    O. Ararat and B. Aksun-Guvenc, “Development of a Collision Avoidance Algorithm Using Elastic Band Theory,” IFAC Proc. V ol., vol. 41, no. 2, pp. 8520–8525, Jan. 2008, doi: 10.3182/20080706-5-KR-1001.01440

    2008

  56. [66]

    Path Planning and Robust Control of Autonomous Vehicles,

    S. Zhu, “Path Planning and Robust Control of Autonomous Vehicles,” Ph.D., The Ohio State University, United States -- Ohio, 2020. Accessed: Oct. 24, 2023. [Online]. Available: https://www.proquest.com/docview/2612075055/abstract/73982D6BAE3D419APQ/1

    2020

  57. [67]

    Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines,

    M. Morsali, E. Frisk, and J. Åslund, “Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines,” IEEE Trans. Intell. Veh., vol. 6, no. 4, pp. 611–621, Dec. 2021, doi: 10.1109/TIV .2020.3042087

    2021

  58. [68]

    Deep Reinforcement Learning for Autonomous Driving: A Survey,

    B. R. Kiran et al., “Deep Reinforcement Learning for Autonomous Driving: A Survey,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 4909–4926, Jun. 2022, doi: 10.1109/TITS.2021.3054625

    2022

  59. [69]

    Emergency Obstacle Avoidance Trajectory Planning Method of Intelligent Vehicles Based on Improved Hybrid A*,

    G. Chen et al., “Emergency Obstacle Avoidance Trajectory Planning Method of Intelligent Vehicles Based on Improved Hybrid A*,” SAE Int. J. Veh. Dyn. Stab. NVH, vol. 8, no. 1, pp. 10-08-01–0001, Nov. 2023, doi: 10.4271/10-08-01-0001

    2023 doi

  60. [70]

    A Survey of Deep RL and IL for Autonomous Driving Policy Learning,

    Z. Zhu and H. Zhao, “A Survey of Deep RL and IL for Autonomous Driving Policy Learning,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 14043–14065, Sep. 2022, doi: 10.1109/TITS.2021.3134702

    2022

  61. [71]

    A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles,

    F. Ye, S. Zhang, P. Wang, and C.- Y . Chan, “A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles,” in 2021 IEEE Intelligent Vehicles Symposium (IV), Jul. 2021, pp. 1073–1080. doi: 10.1109/IV48863.2021.9575880

    2021

  62. [72]

    Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving,

    E. Yurtsever, L. Capito, K. Redmill, and U. Ozguner, “Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving,” May 19, 2020, arXiv: arXiv:2002.00434. Accessed: Nov. 05, 2024. [Online]. Available: 82 http://arxiv.org/abs/2002.00434

    2020 arXiv

  63. [73]

    Learning to Drive in a Day,

    A. Kendall et al., “Learning to Drive in a Day,” Sep. 11, 2018, arXiv: arXiv:1807.00412. doi: 10.48550/arXiv.1807.00412

    2018 doi arXiv

  64. [74]

    Deep reinforcement‐learning‐based driving policy for autonomous road vehicles - Makantasis - 2020 - IET Intelligent Transport Systems - Wiley Online Library

    “Deep reinforcement‐learning‐based driving policy for autonomous road vehicles - Makantasis - 2020 - IET Intelligent Transport Systems - Wiley Online Library.” Accessed: Oct. 24, 2023. [Online]. Available: https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-its.2019.0249

    2020

  65. [75]

    A Safety-Critical Decision-Making and Control Framework Combining Machine-Learning-Based and Rule-Based Algorithms,

    A. Aksjonov and V . Kyrki, “A Safety-Critical Decision-Making and Control Framework Combining Machine-Learning-Based and Rule-Based Algorithms,” SAE Int. J. Veh. Dyn. Stab. NVH, vol. 7, no. 3, pp. 10-07-03–0018, Jun. 2023, doi: 10.4271/10-07-03-0018

    2023 doi

  66. [76]

    Autonomous Highway Driving using Deep Reinforcement Learning,

    S. Nageshrao, H. E. Tseng, and D. Filev, “Autonomous Highway Driving using Deep Reinforcement Learning,” in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Oct. 2019, pp. 2326–2331. doi: 10.1109/SMC.2019.8914621

    2019

  67. [77]

    Reinforced Damage Minimization in Critical Events for Self-driving Vehicles:,

    F. Merola, F. Falchi, C. Gennaro, and M. Di Benedetto, “Reinforced Damage Minimization in Critical Events for Self-driving Vehicles:,” in Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Online St...

    2022 doi

  68. [78]

    Reinforcement Learning based Control of Imitative Policies for Near - Accident Driving,

    Z. Cao et al., “Reinforcement Learning based Control of Imitative Policies for Near - Accident Driving,” Jun. 30, 2020, arXiv: arXiv:2007.00178. doi: 10.48550/arXiv.2007.00178

    2020 doi arXiv

  69. [79]

    End- to-End Autonomous Driving Through Dueling Double Deep Q - Network,

    B. Peng et al., “End- to-End Autonomous Driving Through Dueling Double Deep Q - Network,” Automot. Innov., vol. 4, no. 3, pp. 328–337, Aug. 2021, doi: 10.1007/s42154- 021-00151-3

    2021 doi

  70. [80]

    Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance,

    S. H. Ashwin and R. Naveen Raj, “Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance,” Int. J. Inf. Technol., vol. 15, no. 7, pp. 3541–3553, Oct. 2023, doi: 10.1007/s41870-023-01412-6

    2023 doi

  71. [81]

    End- to-End Race Driving with Deep Reinforcement Learning,

    M. Jaritz, R. de Charette, M. Toromanoff, E. Perot, and F. Nashashibi, “End- to-End Race Driving with Deep Reinforcement Learning,” Aug. 31, 2018, arXiv: arXiv:1807.02371. doi: 10.48550/arXiv.1807.02371

    2018 doi arXiv

  72. [82]

    Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning,

    L. Dinh, P. T. A. Quang, and J. Leguay, “Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning,” Jan. 10, 2024, arXiv: arXiv:2401.05525. doi: 10.48550/arXiv.2401.05525

    2024 doi arXiv

  73. [83]

    Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles,

    A. J. M. Muzahid, S. F. Kamarulzaman, Md. A. Rahman, and A. H. Alenezi, “Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles,” IEEE Access, vol. 10, pp. 43303– 43319, 2022, doi: 10.1109/...

    2022

  74. [84]

    Deep -Reinforcement-Learning- Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety,

    H. Chen, X. Cao, L. Guvenc, and B. Aksun- Guvenc, “Deep -Reinforcement-Learning- Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety,” Electronics, vol. 13, no. 10, Art. no. 10, Jan. 2024, doi: 10.3390/electronics13101952

    2024 doi

  75. [85]

    H. Chen, F. Zhang, and B. Aksun-Guvenc, “Collision Avoidance in Autonomous Vehicles Using the Control Lyapunov Function–Control Barrier Function–Quadratic Programming Approach with Deep Reinforcement Learning Decision-Making,” Electronics, vol. 14, no. 3, Art. no. 3, Jan. 2025...

    2025 doi

  76. [86]

    Optimization of On- Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning,

    K. Meneses -Cime, B. Aksun Guvenc, and L. Guvenc, “Optimization of On- Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning,” Sensors, vol. 22, no. 21, Art. no. 21, Jan. 2022, doi: 10.3390/s22218317

    2022 doi

  77. [87]

    Contact -Aware Safety in Soft Robots Using High- Order Control Barrier and Lyapunov Functions,

    K. Wong, M. Stölzle, W. Xiao, C. D. Santina, D. Rus, and G. Zardini, “Contact -Aware Safety in Soft Robots Using High- Order Control Barrier and Lyapunov Functions,” May 05, 2025, arXiv: arXiv:2505.03841. doi: 10.48550/arXiv.2505.03841. 83

    2025 doi

  78. [88]

    Q-learning,

    C. J. C. H. Watkins and P. Dayan, “Q-learning,” Mach. Learn., vol. 8, no. 3, pp. 279–292, May 1992, doi: 10.1007/BF00992698

    1992 doi

  79. [89]

    Autonomous Vehicle Decision- Making with Policy Prediction for Handling a Round Intersection,

    X. Li, L. Guvenc, and B. Aksun- Guvenc, “Autonomous Vehicle Decision- Making with Policy Prediction for Handling a Round Intersection,” Electronics, vol. 12, no. 22, Art. no. 22, Jan. 2023, doi: 10.3390/electronics12224670

    2023 doi

  80. [90]

    Human-level control through deep reinforcement learning,

    V . Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015, doi: 10.1038/nature14236

    2015 doi

  81. [91]

    Playing Atari with Deep Reinforcement Learning,

    V . Mnih et al., “Playing Atari with Deep Reinforcement Learning,” Dec. 19, 2013, arXiv: arXiv:1312.5602. doi: 10.48550/arXiv.1312.5602

    2013 doi arXiv

  82. [92]

    Dueling Network Architectures for Deep Reinforcement Learning,

    Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas, “Dueling Network Architectures for Deep Reinforcement Learning,” Apr. 05, 2016, arXiv: arXiv:1511.06581. doi: 10.48550/arXiv.1511.06581

    2016 doi arXiv

  83. [93]

    Deep Reinforcement Learning with Double Q - learning,

    H. van Hasselt, A. Guez, and D. Silver, “Deep Reinforcement Learning with Double Q - learning,” Dec. 08, 2015, arXiv: arXiv:1509.06461. doi: 10.48550/arXiv.1509.06461

    2015 doi arXiv

  84. [94]

    Policy Gradient Methods for Reinforcement Learning with Function Approximation,

    R. S. Sutton, D. McAllester, S. Singh, and Y . Mansour, “Policy Gradient Methods for Reinforcement Learning with Function Approximation,” in Advances in Neural Information Processing Systems, MIT Press, 1999. Accessed: Nov. 05, 2024. [Online]. Available: https://proceedings.ne...

    1999

  85. [95]

    Simple statistical gradient- following algorithms for connectionist reinforcement learning,

    R. J. Williams, “Simple statistical gradient- following algorithms for connectionist reinforcement learning,” Mach. Learn., vol. 8, no. 3, pp. 229–256, May 1992, doi: 10.1007/BF00992696

    1992 doi

  86. [96]

    Proximal Policy Optimization Algorithms,

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” Aug. 28, 2017, arXiv: arXiv:1707.06347. doi: 10.48550/arXiv.1707.06347

    2017 doi arXiv

  87. [97]

    Asynchronous Methods for Deep Reinforcement Learning,

    V . Mnih et al., “Asynchronous Methods for Deep Reinforcement Learning,” Jun. 16, 2016, arXiv: arXiv:1602.01783. doi: 10.48550/arXiv.1602.01783

    2016 doi arXiv

  88. [98]

    Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,

    T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,” Aug. 08, 2018, arXiv: arXiv:1801.01290. doi: 10.48550/arXiv.1801.01290

    2018 doi arXiv

  89. [99]

    Continuous control with deep reinforcement learning,

    T. P. Lillicrap et al., “Continuous control with deep reinforcement learning,” Jul. 05, 2019, arXiv: arXiv:1509.02971. doi: 10.48550/arXiv.1509.02971

    2019 doi arXiv

  90. [100]

    Leurent, An Environment for Autonomous Driving Decision- Making

    E. Leurent, An Environment for Autonomous Driving Decision- Making. (May 2018). Python. Accessed: Jun. 14, 2025. [Online]. Available: https://github.com/eleurent/highway-env

    2018

  91. [101]

    Addressing Function Approximation Error in Actor-Critic Methods,

    S. Fujimoto, H. van Hoof, and D. Meger, “Addressing Function Approximation Error in Actor-Critic Methods,” Oct. 22, 2018, arXiv: arXiv:1802.09477. doi: 10.48550/arXiv.1802.09477

    2018 doi arXiv

  92. [102]

    A Novel Integrated Simulation and Testing 84 Platform for Self-Driving Cars With Hardware in the Loop,

    S. Chen, N.-N. Zheng, Y . Chen, and S. Zhang, “A Novel Integrated Simulation and Testing 84 Platform for Self-Driving Cars With Hardware in the Loop,” IEEE Trans. Intell. Veh., vol. PP, pp. 1–1, May 2019, doi: 10.1109/TIV .2019.2919470

    2019

  93. [103]

    Integrating tools for an effective testing of connected and automated vehicles technologies,

    L. Pariota et al., “Integrating tools for an effective testing of connected and automated vehicles technologies,” IET Intell. Transp. Syst., vol. 14, no. 9, pp. 1025–1033, 2020, doi: 10.1049/iet-its.2019.0678

    2020

  94. [104]

    Vehicle -in-Virtual- Environment Method for ADAS and Connected and Automated Driving Function Development, Demonstration and Evaluation,

    X. Cao, H. Chen, S. Y . Gelbal, B. A ksun-Guvenc, and L. Guvenc, “Vehicle -in-Virtual- Environment Method for ADAS and Connected and Automated Driving Function Development, Demonstration and Evaluation,” SAE International, Warrendale, PA, SAE Technical Paper 2024-01–1967, Apr....

    2024 doi

  95. [105]

    A connected and autonomous vehicle hardware -in-the-loop simulator for developing automated driving algorithms,

    S. Y . Gelbal, S. Tamilarasan, M. R. Canta s, L. G uvenc, and B. Aksun- Guvenc, “A connected and autonomous vehicle hardware -in-the-loop simulator for developing automated driving algorithms,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. ...

    2017

  96. [106]

    Developing a Vehicle -in-Virtual- Environment (VVE) Based Autonomous Driving Function Development and Evaluation Pipeline for Vulnerable Road User Safety,

    H. Chen, X. Cao, L. Guvenc, and B. A ksun-Guvenc, “Developing a Vehicle -in-Virtual- Environment (VVE) Based Autonomous Driving Function Development and Evaluation Pipeline for Vulnerable Road User Safety,” SAE International, Warrendale, PA, SAE Technical Paper 2025-01–8061, A...

    2025 doi

  97. [107]

    Mobile Safety Application for Pedestrians Utilizing P2V Communication over Bluetooth,

    S. Y . Gelbal, M. R. Cantas, B. A ksun-Guvenc, L. Guvenc, G. Surnilla, and H. Zhang, “Mobile Safety Application for Pedestrians Utilizing P2V Communication over Bluetooth,” SAE International, Warrendale, PA, SAE Technical Paper 2022-01–0155, Mar. 2022. doi: 10.4271/2022-01-0155

    2022 doi

  98. [109]

    A review and a comparison of Dugoff and modified Dugoff formula with Magic formula,

    A. Bhoraskar and P. Sakthivel, “A review and a comparison of Dugoff and modified Dugoff formula with Magic formula,” in 2017 International Conference on Nascent Technologies in Engineering (ICNTE), Jan. 2017, pp. 1–4. doi: 10.1109/ICNTE.2017.7947898

    2017

  99. [2652]

    doi: 10.1109/ITSC48978.2021.9564990

    2021

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.