Pith. sign in

REVIEW 3 major objections 4 minor 49 references

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

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A staged MIL-HIL-VVE pipeline can validate pedestrian-safe autonomous driving before public road testing.

desk verdict The paper is an honest status report, but the load-bearing result -- the integrated MIL-HIL-VVE pipeline -- is never run end-to-end; the VVE stage only shows manual trajectory sync, and the authors admit the DRL-in-VVE test is still in progress. read the letter →

arxiv 2501.06113 v1 pith:Z4JURFIA submitted 2025-01-10 cs.RO cs.SYeess.SY

Haochong Chen , Xincheng Cao , Levent Guvenc , Bilin Aksun Guvenc This is my paper · ORCID
classification cs.ROcs.SYeess.SY
keywords autonomousdrivingvulnerableroadusersafetyVehicle-in-Virtual-Environmenthardware-in-the-loopmodel-in-the-loopdeepreinforcementlearningcollisionavoidancevehicledynamics
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 paper proposes a development and evaluation pipeline for autonomous driving functions that runs sequentially through Model-in-the-Loop (MIL) simulation, Hardware-in-the-Loop (HIL) testing, and Vehicle-in-Virtual-Environment (VVE) testing before any public road deployment. The intended payoff is that vulnerable road users—pedestrians in particular—are never involuntarily exposed to experimental self-driving algorithms. The pipeline is demonstrated with a double deep Q-network (DDQN) agent trained to brake for pedestrians in the MIL stage, then checked on real control hardware in HIL, while the VVE stage synchronizes a real vehicle's motion into a virtual traffic scenario. The paper claims all these tests confirm the feasibility and effectiveness of the proposed methods, with the DRL-based collision avoidance test in VVE stated as still in progress.

What carries the argument

The key mechanism is the Vehicle-in-Virtual-Environment (VVE) synchronization loop: a real vehicle operating in a safe open space has its position and heading measured by an RTK GPS unit, passed to the onboard MicroAutobox controller, and streamed over Ethernet UDP to an in-vehicle PC running an Unreal Engine-based CARLA virtual environment, where a frame transformation maps the real motion into the virtual vehicle's motion; virtual sensor data then flows back to the real control unit so the vehicle reacts to virtual pedestrians and other actors. This loop is what lets real vehicle dynamics and real pedestrian behavior participate in repeatable, editable traffic scenarios without any public-road exposure. The supporting machinery includes the extended single-track vehicle model with longitudinal and lateral dynamics, a Modified Dugoff tire model and wheel rotation model that generate tire forces, and the DDQN agent whose state space combines an occupancy grid, ego-vehicle status, path waypoints, and time-to-collision zones.

What would settle it

Run the MIL-trained DDQN agent in the VVE setup with a real pedestrian walking across the crosswalk, and measure the virtual time-to-collision and the real braking profile; if the vehicle fails to keep time-to-collision above four seconds as it did in MIL, or if the real trajectory diverges from the simulated one at low approach speed, the claimed transfer and pipeline effectiveness are contradicted.

Watch

Extended reading notes

Core claim

The central claim is that combining MIL, HIL, and VVE into one sequential testing pipeline can comprehensively develop and validate autonomous driving functions for vulnerable road user safety, replacing the risky practice of continuing development on public roads. In the MIL stage, a DDQN-based agent trained in a CARLA virtual environment against an extended single-track vehicle model with a Modified Dugoff tire model learns to decelerate and stop for crossing pedestrians, converging after roughly 800 episodes and keeping time-to-collision above four seconds. In HIL, the same trained agent tracks the desired braking profile in real time on a MicroAutobox controller connected to the vehicle model over CAN and Ethernet UDP. In VVE, a real vehicle's RTK-GPS position and heading are transformed into the virtual world so its trajectory overlaps with the virtual vehicle's, allowing real pedestrians equipped with a Bluetooth low-energy mobile app to act as virtual road users. The paper states that all these tests confirm the feasibility and effectiveness of the proposed experimental methods, and that the full DRL collision avoidance test in the VVE environment is still in progress.

Load-bearing premise

The whole pipeline depends on the assumption that the low-order bicycle vehicle model with Modified Dugoff tires is accurate enough that a braking policy trained on it in MIL will still behave correctly on the real vehicle in HIL and VVE without retraining.

Editorial extensions

If this is right

  • Developers can validate VRU collision avoidance functions through simulation, hardware, and real-vehicle-in-virtual-environment stages before any public road testing, cutting cost and risk.
  • Rare and safety-critical traffic scenarios (e.g., a pedestrian stepping out) can be tested repeatably by editing the virtual environment rather than waiting for real-world encounters.
  • The pipeline supports multi-actor tests: real pedestrians using a Bluetooth low-energy phone app can be synchronized into the virtual scene for V2P collision avoidance.
  • If the transfer claim holds, a DRL agent trained entirely in MIL can be evaluated on real hardware (HIL) and in a real vehicle (VVE) without retraining or performance loss.
  • Passing all three stages would raise confidence that an algorithm is ready for a final, shorter public road validation phase.

Reading between the lines

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

  • The paper's results demonstrate each stage individually, but the end-to-end claim that one DRL agent transfers unchanged through MIL, HIL, and VVE still depends on the in-progress VVE collision avoidance experiment; until that runs, the pipeline's full transfer is an inference.
  • A quantitative synchronization error between the real and virtual vehicle trajectories would strengthen the VVE validation; the paper currently reports only a visual overlap.
  • If the near-zero-speed singularities of the bicycle model appear in practice, the agent's emergency braking behavior at low approach speeds could differ between simulation and the real vehicle, so a higher-fidelity vehicle model would make the pipeline's transfer claim more robust.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes a staged testing and development pipeline for autonomous driving functions, combining Model-in-the-Loop (MIL), Hardware-in-the-Loop (HIL), and Vehicle-in-Virtual-Environment (VVE) testing before public road deployment, with a focus on vulnerable road user (VRU) safety. The authors present an extended single-track vehicle model with a Modified Dugoff tire model, train a Double Deep Q-Network (DDQN) collision-avoidance agent in a CARLA-based MIL setup, evaluate it in a HIL setup, and demonstrate a VVE motion-synchronization test using manual driving. The conclusion states that all tests confirm the feasibility and effectiveness of the proposed pipeline, while also noting that a full test of the DRL-based collision-avoidance algorithm in the VVE stage is still in progress.

Significance. If the integrated MIL-HIL-VVE pipeline were fully demonstrated, it would be a practically valuable contribution to safe and resource-efficient validation of VRU-related autonomous driving functions, potentially reducing the need for risky public-road testing during development. The manuscript gives a detailed account of the vehicle dynamic model and the DDQN training setup, and it is transparent about the current lack of a complete DRL-in-VVE experiment. However, the paper's central claim of pipeline effectiveness is not yet supported by the reported evidence, because the VVE stage only demonstrates manual-driving synchronization and does not exercise the trained collision-avoidance agent that was used in the MIL and HIL stages.

major comments (3)
  1. [Vehicle-In-Virtual-Environment Test; Conclusion] The VVE test reported in the 'Vehicle-In-Virtual-Environment Test' section (Figure 18) evaluates only manual-driving trajectory synchronization between the real and virtual vehicles. The DDQN collision-avoidance agent trained in the MIL stage and evaluated in the HIL stage is never run in the VVE stage. The Conclusion explicitly states: 'Due to time constraints, a full test of the deep reinforcement learning (DRL)-based collision avoidance algorithm is still in progress and will be included in the updated version of the paper.' Because the same AD function is not carried through MIL, HIL, and VVE, the claimed MIL-to-HIL-to-VVE integration is not demonstrated. The statement 'All these tests confirm the feasibility and effectiveness of our proposed experimental methods' is therefore internally inconsistent with the reported results. The authors should either include the VVE evaluation of the trained DRL agent or clearly restrict the paper's claims to a partial demonstration of the pipeline components.
  2. [Model-in-Loop Test; Future Work] The MIL section states that the vehicle dynamic model 'closely mirrors the dynamics of a real vehicle' and that training results are 'highly reliable and can be easily applied to HIL and VVE testing environments.' The Future Work section, however, acknowledges that the bicycle model was chosen 'because our primary objective was to demonstrate the effectiveness of the proposed testing pipeline rather than to achieve high-fidelity vehicle dynamics simulation' and that singularities at zero speed 'restrict the effectiveness of MIL testing.' These statements are in tension, and the claimed transferability from MIL to HIL and VVE is not supported by any quantitative comparison of the simulation model against real-vehicle data. The authors should either provide such a validation or temper the transferability claims to match the acknowledged low-fidelity modeling choice.
  3. [Conclusion; Hardware-in-Loop Test] The Conclusion says 'We then showcased how to evaluate traditional path-following control algorithms using the HIL setup,' but the HIL experiment reported in the 'Hardware-in-Loop Test' section (Figure 17) is an evaluation of the DDQN-based agent's speed-tracking and braking behavior, not a traditional path-following controller. This discrepancy makes it unclear which AD function actually completed the HIL stage and which function is claimed to have progressed toward VVE. The authors should clarify whether the HIL results correspond to the DRL agent or to a separate classical controller, and align the Conclusion accordingly.
minor comments (4)
  1. [Conclusion] The opening sentence of the Conclusion repeats the phrase 'In order to address challenges caused by urbanization and' twice; this should be corrected.
  2. [References; Deep Reinforcement Learning Based Collision Avoidance] The DQN citations appear as '[30-31]' in the text, but the actual Mnih et al. references are numbered [35] and [36]; the in-text citation numbers should be corrected.
  3. [Deep Reinforcement Learning Based Collision Avoidance; Experiments and Results] The manuscript introduces 'time-to-collision-zones (TTZ)' earlier but later defines 'TTZ' as 'Time-To-Collision'; please use one consistent definition and abbreviation throughout.
  4. [General] Several figures (Figures 7, 8, 9, 10, 11, 12, and 18) are explicitly taken from the authors' prior work (references [16] and [38]). The manuscript should state more clearly which elements are new in this submission and which are reused from earlier publications.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the MIL/HIL/VVE experiments are reported in-paper; self-citations are tooling, not proof.

full rationale

The derivation chain is not circular. The vehicle dynamics in Equations (1)-(6) are standard single-track and Modified Dugoff formulations taken from cited literature, and they do not encode the paper's own success criteria. The DDQN agent is trained in the MIL stage and evaluated with reported reward, speed-tracking, and TTZ measurements in both MIL and HIL; these are new in-paper evaluations, not fitted inputs renamed as predictions. The VVE stage reports an actual manual-driving synchronization measurement (Figure 18), so the pipeline's VVE leg rests on in-paper data rather than solely on the authors' prior VVE papers ([15], [16]). Citing the authors' previous DDQN architecture ([38]) and VVE architecture ([16]) is a normal use of prior work, and no load-bearing step reduces to a self-citation chain or to an equation that is equivalent to its own input. The conclusion's statement that 'All these tests confirm the feasibility and effectiveness of our proposed experimental methods' is stronger than the reported evidence, since the paper explicitly states that a full DRL-based collision-avoidance VVE test is still in progress; however, that is an evidence/consistency gap, not circularity. Overall circularity score is therefore 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No fitted numeric outputs are derived; the paper's contribution is a testing workflow. The free parameters listed are the unreported values needed to reproduce the experiments. The assumptions above are the load-bearing background choices for the claimed pipeline, and none are validated with independent data in the paper.

free parameters (2)
  • Vehicle model and tire parameters (mass, geometry, inertias, stiffnesses, friction) = not reported
    The MIL and HIL simulations depend on these values, but the paper does not list them, so the experiments cannot be reproduced without selecting them by hand.
  • DDQN training hyperparameters = not reported
    Training is described qualitatively (1,500 episodes, reward converging around 0.3), but no learning rate, batch size, discount factor, or exploration schedule is reported.
assumptions (4)
  • domain assumption The single-track bicycle model with Modified Dugoff tire forces is sufficiently accurate for the MIL and HIL stages.
    The paper explicitly states it chose the bicycle model to demonstrate the pipeline rather than to achieve high-fidelity vehicle dynamics. If the model is too inaccurate, the policy learned in MIL may not transfer to HIL or VVE.
  • domain assumption The CARLA virtual environment is a sufficiently realistic representation of traffic for training and evaluation.
    The paper uses CARLA without validating its sensor and physics fidelity against real-world measurements or against the later VVE environment.
  • domain assumption Frame transformation synchronization between the real and virtual vehicle is accurate enough for safety-critical collision avoidance testing.
    The VVE test only shows overlapped trajectories in one manual driving test; no quantitative synchronization error or latency analysis is provided.
  • domain assumption The two-pedestrian crosswalk scenario is representative of the vulnerable road user safety cases the pipeline is claimed to cover.
    Only this scenario is tested, with no scenario diversity, edge-case coverage, or statistical sampling to support generalization.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Vehicle-in-Virtual-Environment (VVE) Based Autonomous Driving Function Development and Evaluation Methodology for Vulnerable Road User Safety." pith.science (2026). https://pith.science/paper/Z4JURFIA

@misc{pith2026250106113,
  author       = {Pith},
  title        = {Pith review of: Vehicle-in-Virtual-Environment (VVE) Based Autonomous Driving Function Development and Evaluation Methodology for Vulnerable Road User Safety},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z4JURFIA}},
  note         = {Machine review of arXiv:2501.06113}
}
read the original abstract

Traditional methods for developing and evaluating autonomous driving functions, such as model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations, heavily depend on the accuracy of simulated vehicle models and human factors, especially for vulnerable road user safety systems. Continuation of development during public road deployment forces other road users including vulnerable ones to involuntarily participate in the development process, leading to safety risks, inefficiencies, and a decline in public trust. To address these deficiencies, the Vehicle-in-Virtual-Environment (VVE) method was proposed as a safer, more efficient, and cost-effective solution for developing and testing connected and autonomous driving technologies by operating the real vehicle and multiple other actors like vulnerable road users in different test areas while being immersed within the same highly realistic virtual environment. This VVE approach synchronizes real-world vehicle and vulnerable road user motion within the same virtual scenario, enabling the safe and realistic testing of various traffic situations in a safe and repeatable manner. In this paper, we propose a new testing pipeline that sequentially integrates MIL, HIL, and VVE methods to comprehensively develop and evaluate autonomous driving functions. The effectiveness of this testing pipeline will be demonstrated using an autonomous driving path-tracking algorithm with local deep reinforcement learning modification for vulnerable road user collision avoidance.

Figures

Figures reproduced from arXiv: 2501.06113 by the authors.

Figure 2
Figure 2. Extended lateral dynamic model represented in vehicle [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Wheel rotation model [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Overall vehicle model structure Hardware-in-Loop Test Hardware-in-the-Loop (HIL) testing is an exceptionally effective simulation and validation technique widely employed in the development and testing of autonomous driving functions. The core principle of HIL testing involves integrating actual hardware components in vehicle, such as autonomous driving controllers (MicroAutobox), GPS, and dedicated short-range comm… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

49 extracted references · 15 canonical work pages

  1. [38]

    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

    doi:10.3390/electronics13101952 2024

  2. [1]

    Connected and Autonomous Vehicles,

    L. Guvenc, B. A. 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

    doi:10.1002/9781119173601.ch35 2017

  3. [2]

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

    S. Y. Gelbal, B. A. 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 SCOPE ’17. New York, NY, USA: Association for Computing Machinery, 2017, pp. 57 –62. do...

    arXiv 2017

  4. [3]

    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

    work page

  5. [4]

    World Health Organization, Global status report on road safety

    work page

  6. [5]

    813428.pdf

    “813428.pdf.” Accessed: Aug. 25, 2024. [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813428

    work page 2024

  7. [6]

    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

    doi:10.1177/154193120404801661 2004

  8. [7]

    J3016_202104: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles - SAE International

    “J3016_202104: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles - SAE International.” Accessed: Oct. 24, 2023. [Online]. Available: https://www.sae.org/standards/content/j3016_202104/

    work page 2023

Show all 49 references
  1. [8]

    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

  2. [9]

    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

  3. [10]

    A Survey of Intelligent Driving Vehicle Trajectory Tracking Based on Vehicle Dynamics,

    Y. Zha, J. Deng, Y. Qiu, K. Zhang, and Y. Wang, “A Survey of Intelligent Driving Vehicle Trajectory Tracking Based on Vehicle Dynamics,” SAE Int. J. Veh. Dyn. Stab. NVH, vol. 7, no. 2, Art. no. 10- 07-02–0014, May 2023, doi: 10.4271/10-07-02-0014. Page 11 of 13

    2023 doi

  4. [11]

    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

  5. [12]

    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

  6. [13]

    A Novel Integrated Simulation and Testing 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 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

  7. [14]

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

    Ş. Y. Gelbal, S. Tamilarasan, M. R. Cantaş, L. Güvenç, and B. Aksun-Güvenç, “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. 20...

    2017

  8. [15]

    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

  9. [16]

    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. 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. ...

    2024 doi

  10. [17]

    Mobile Safety Application for Pedestrians Utilizing P2V Communication over Bluetooth,

    S. Y. Gelbal, M. R. Cantas, B. A. 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

  11. [18]

    Guvenc, B

    L. Guvenc, B. Aksun-Guvenc, S. Zhu, S.Y. Gelbal, 202 1, Autonomous Road Vehicle Path Planning and Tracking Control , |Wiley / IEEE Press

  12. [19]

    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

  13. [20]

    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

  14. [21]

    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

  15. [22]

    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

  16. [23]

    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

  17. [24]

    Collision Cone Control Barrier Functions for Kinematic Obstacle Avoidance in UGVs,

    P. Thontepu et al., “Collision Cone Control Barrier Functions for Kinematic Obstacle Avoidance in UGVs,” in 2023 Ninth Indian Control Conference (ICC), Dec. 2023, pp. 293 –298. doi: 10.1109/ICC61519.2023.10442173

    2023

  18. [25]

    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

  19. [26]

    Learning to Drive in a Day,

    A. Kendall et al. , “Learning to Drive in a Day,” in 2019 International Conference on Robotics and Automation (ICRA) , May 2019, pp. 8248–8254. doi: 10.1109/ICRA.2019.8793742

    2019

  20. [27]

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

    E. Yurtsever, L. Capito, K. Redmill, and U. Ozgune, “Integrating Deep Reinforcement Learning with Model -based Path Planners for Automated Driving,” in 2020 IEEE Intelligent Vehicles Symposium (IV), Oct. 2020, pp. 1311–1316. doi: 10.1109/IV47402.2020.9304735

    2020

  21. [28]

    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

  22. [29]

    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

  23. [30]

    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

  24. [31]

    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

  25. [32]

    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

  26. [33]

    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

  27. [34]

    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. Page 12 of 13

    2020 doi arXiv

  28. [35]

    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

  29. [36]

    Human-level control through deep reinforcement learning,

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

    2015 doi

  30. [37]

    Deep Reinforcement Learning with Double Q -Learning,

    H. van Hasselt, A. Guez, and D. Silver, “Deep Reinforcement Learning with Double Q -Learning,” Proc. AAAI Conf. Artif. Intell., vol. 30, no. 1, Art. no. 1, Mar. 2016, doi: 10.1609/aaai.v30i1.10295

    2016 doi

  31. [40]

    Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds,

    E. Lowe, L. Guvenc, "Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds," Electronics, Vol. 12, pp. 4765, 2023, https://doi.org/10.3390/electronics12234765

    2023 doi

  32. [41]

    V2X Communication between Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs),

    O. Kavas-Torris et al, "V2X Communication between Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs)," Sensors, Vol. 22, No. 22, pp. 8941, 2022, https://doi.org/10.3390/s22228941

    2022 doi

  33. [42]

    Adaptive Event-Triggered Platoon Control Under Unreliable Communication Links,

    J. Wang et al, "Adaptive Event-Triggered Platoon Control Under Unreliable Communication Links," in IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 3, pp. 1924-1935, March 2022, doi: 10.1109/TITS.2020.3030016

    1924

  34. [43]

    Elastic band based pedestrian collision avoidance using V2X communication,

    S. Y. Gelbal, S. Arslan, H. Wang, B. Aksun -Guvenc and L. Guvenc, "Elastic band based pedestrian collision avoidance using V2X communication," 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, 2017, pp. 270- 276, doi: 10.1109/IVS.2017.7995731

    2017

  35. [44]

    Gelbal, B

    S.Y. Gelbal, B. Aksun-Guvenc, L. Guvenc, Collision Avoidance of Low Speed Autonomous Shuttles with Pedestrians. Int.J Automot. Technol. 21, 903 –917 (2020). https://doi.org/10.1007/s12239-020 - 0087-7

    2020 doi

  36. [45]

    Socially Acceptable Collision Avoidance System for Vulnerable Road Users,

    M.T. Emirler, H. Wang, B. Aksun-Guvenc, "Socially Acceptable Collision Avoidance System for Vulnerable Road Users," IFAC - PapersOnLine, Volume 49, Issue 3, 2016, Pages 436-441, ISSN 2405- 8963, https://doi.org/10.1016/j.ifacol.2016.07.073

    2016 doi

  37. [46]

    Development of a Collision Avoidance Algorithm Using Elastic Band Theory,

    O. Ararat, B. Aksun -Guvenc, "Development of a Collision Avoidance Algorithm Using Elastic Band Theory," IFAC Proceedings Volumes, Volume 41, Issue 2, 2008, Pages 8520- 8525, ISSN 1474 - 6670, ISBN 9783902661005, https://doi.org/10.3182/20080706 -5- KR-1001.01440. Acknowledgme...

    2008

  38. [266]

    doi: 10.5220/0010908000003124

    doi

  39. [2015]

    Accessed: Oct

    Geneva: World Health Organization, 2015. Accessed: Oct. 24,

    2015

  40. [2018]

    doi: 10.4271/2018-01-1182

    2018 doi

  41. [2023]

    Available: https://iris.who.int/handle/10665/189242

    [Online]. Available: https://iris.who.int/handle/10665/189242

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.