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REVIEW 5 major objections 6 minor 44 references

Scene Modeling of Autonomous Vehicles Avoiding Stationary and Moving Vehicles on Narrow Roads

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A narrow-road scene model lets an autonomous vehicle identify meeting gaps and choose cut-in, meet, or back-up maneuvers, with high pass rates in simulation and small-scale tests.

desk verdict A transparent geometric model for narrow-road meetings, but the robustness claim rests on an unverified assumption about oncoming-vehicle behavior; worth a serious referee with stiffer tests. read the letter →

arxiv 2412.13305 v2 pith:CRGIG3SB submitted 2024-12-17 cs.RO

Qianyi Zhang , Jinzheng Guang , Zhenzhong Cao , Jingtai Liu This is my paper · ORCID
classification cs.RO
keywords narrowroadnavigationautonomousdrivingmeetinggapidentificationwidthoccupancyminimizationhomologyclassmotionplanningdecisionmakingscenemodeling
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 claims that a narrow-road standoff between an autonomous vehicle and an oncoming vehicle can be solved by first compressing the scene into two expanded boundaries, the paths the two vehicles would trace if they hugged the road edge and every parked car, and then reading the gaps between those boundaries as places where the vehicles can pass. On that geometric foundation the authors build a decision-making loop that rates candidate meeting gaps, assigns each candidate maneuver to a homology class (cut in, meet, back up, or advance), optimizes one trajectory per class, and picks the best one. The authors report that this approach, called SM-NR, reaches high pass rates in simulated and small-scale real-world scenarios, and that it switches gaps or maneuvers when the oncoming vehicle behaves aggressively or hesitates. A sympathetic reader would care because the paper gives a parameter-light, interpretable alternative to sampling-heavy planners for a common bottleneck that causes freezing-robot behavior in existing systems.

What carries the argument

The load-bearing object is the pair of expanded boundaries $L_{av}^{model}$ and $L_{mv}^{model}$, rear-center paths inflated by the vehicle's width and length, constructed by connecting circular detours of radius equal to the minimum turning radius around the corner points of stationary vehicles. Where the two inflated boundaries overlap along the road axis, the paper defines a non-meeting area; every remaining interval is a candidate meeting gap, with cutting-in and backing-up maneuvers generating additional gaps. A homology-class test, taken from the cited criterion, labels each candidate trajectory by the quadrant of its endpoint and requires the optimization to stay within that class, so the seven strategies (advance, meet, cut, and back up on each side) keep their intended meaning while being smoothed and time-optimized. The gap-selection cost in Eq. (11) and the hierarchical trajectory evaluation carry the decision-making.

What would settle it

Drive an oncoming vehicle at a fixed lateral offset from the assumed boundary-hugging path, in simulation and on the small-scale platform, through a scenario where the model predicts a meeting gap, and record whether the vehicles collide or the gap shrinks below the vehicle widths. If the pass rate falls sharply with offset, the gap-identification claim is conditional on that assumption.

Watch

Extended reading notes

Core claim

The central discovery is that the entire meeting problem on a narrow road can be expressed as a one-dimensional comparison along the road axis: expand the rear-center path of each vehicle by its body model, and wherever the autonomous vehicle's expanded boundary lies above the oncoming vehicle's expanded boundary at the same longitudinal coordinate, mark a non-meeting area; the complement is a candidate meeting gap. Under the assumption that both vehicles advance tightly alongside stationary vehicles and road edges, these boundaries are built from circular detours around the corner points of parked vehicles, merged into smooth curves. The paper further claims that distinguishing maneuvers by homology class, whether the trajectory's endpoint lies in a given quadrant relative to the gap, preserves the semantic meaning of a maneuver through optimization, so a cut-in stays a cut-in and a back-up stays a back-up. Together these pieces let the vehicle decide where to wait, whether to back up for safety, or cut in for efficiency.

Load-bearing premise

The gap computation assumes both the autonomous vehicle and the oncoming vehicle advance tightly alongside stationary vehicles and road edges, so that two expanded boundaries form; if the oncoming vehicle drives down the middle or away from the edge, the computed meeting gaps may not exist.

Editorial extensions

If this is right

  • On a road where the boundary-hugging assumption holds, the model identifies meeting gaps that spatial-margin methods miss, such as gaps reachable only by cutting in or backing up, and therefore lets the vehicle advance farther before yielding.
  • If the oncoming vehicle accelerates mid-meeting or refuses to cooperate, the cost function's memory terms ($c_{times}$ and the repeat-selection factor) keep the vehicle's gap choice stable, while the flexible endpoint constraints in the cut-in and back-up strategies let it deepen its maneuver adaptively.
  • Because each maneuver carries a homology-class label through optimization, the planner can deliberately choose between semantically distinct behaviors (meet versus back up versus cut in) instead of sampling trajectories blindly, reducing decision oscillation.
  • In the reported comparisons, the approach achieves higher scene pass rates and lower additional travel time than the TEB and P2EG baselines across single-gap, two-gap, tiny-gap, and oncoming-lane scenarios.

Reading between the lines

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

  • If the boundary-hugging assumption fails, the gap geometry is computed wrong; a natural extension is to model the oncoming vehicle's lateral offset as an uncertainty and re-plan conservatively. This is an inference from the paper's stated assumption, not a claim the paper makes.
  • The same gap-versus-homology recipe could transfer to other bottleneck encounters, such as two robots passing in a corridor or vehicles meeting at a narrow bridge, where the environment can be flattened into two boundary curves.
  • A quantitative stress test would sweep the oncoming vehicle's lateral deviation and its speed noise and plot SM-NR's success rate, revealing how much the robustness claim depends on the tight-alongside assumption.
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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

5 major / 6 minor

Summary. The paper presents SM-NR, a scene-modeling and decision-making framework for autonomous vehicles navigating narrow roads with stationary and oncoming vehicles. The method constructs expanded boundaries of the road by modeling detours around stationary vehicles under the assumption that both the autonomous vehicle and the oncoming vehicle advance tightly alongside stationary vehicles and road edges. These boundaries are used to identify meeting gaps and non-meeting areas. An optimal gap is selected via a weighted cost function, and seven candidate trajectories belonging to different homology classes are initialized, optimized, and evaluated. Validation is carried out in four simulation scenarios with 25 runs each against TEB and P2EG, plus small-scale real-vehicle experiments with volunteer-controlled oncoming vehicles. The central claims are that the geometric scene model enables robust gap identification, efficient trajectory selection, and high scene pass rates.

Significance. If the results hold, the paper makes a useful contribution by offering a detailed geometric model of narrow-road meeting scenarios, including explicit derivations for detour boundaries, meeting-gap identification, and homology-class-based trajectory initialization. The authors provide open-source code and a supplemental video, and the real-vehicle experiments with volunteer drivers are a valuable addition beyond pure simulation. The main limitations are the hand-tuned parameters in the gap-selection cost, the lack of a rigorous kinodynamic feasibility proof for optimized trajectories, and the limited statistical power of 25 runs per scenario. Overall, the contribution is promising but not yet fully substantiated.

major comments (5)
  1. [§V-B, Eq. (14)] The kinematic constraint H(pi, pi+1)=0 enforces that the chord between adjacent waypoints is orthogonal to the average orientation, but the paper does not show that the optimized trajectory satisfies the minimum turning radius R. The objective in Eq. (12) minimizes path length at maximum speed and does not include a curvature bound or steering-angle constraint. Since the geometric boundary construction in Sec. IV relies on turns of radius R, the optimized trajectory could have curvature larger than 1/R, making the trajectories kinodynamically infeasible for the Ackermann vehicle described in Sec. III. Please add an explicit curvature constraint to the optimization or prove that the discrete constraint (14), together with the initialization, guarantees a bound on the turning radius.
  2. [§VI-A, Table I] The quantitative evaluation uses only 25 runs per scenario, and no confidence intervals or statistical significance tests are reported. For a binary success rate, 25 runs gives a standard error of up to 10 percentage points, so differences such as 80% vs. 92% may not be meaningful. Furthermore, the rendering of Table I in the manuscript is garbled, with column headers and numbers interleaved in a way that prevents the reader from verifying the reported values. Please provide a clean table and report confidence intervals or additional runs, and clarify what the numerical entries in each cell represent.
  3. [§IV-D and §VI-A] The entire meeting-gap identification depends on the assumption stated in Sec. IV-D that both the autonomous vehicle and the oncoming vehicle advance tightly alongside stationary vehicles and road edges. If the oncoming vehicle deviates laterally, for example by hugging the centerline or leaving extra margin, the boundary Lmodel_mv is not the true swept boundary and the identified gap g* may be too short or incorrectly placed. The qualitative conflict experiment in Fig. 9 shows adaptive behavior when the oncoming vehicle does not cooperate, but the quantitative replay data in Sec. VI-A are recorded from volunteers performing smooth meetings after practice, so the data likely conform to the tight-boundary assumption. The claim of robust gap selection is therefore not tested against the behavior that would invalidate the model. Please include quantitative experiments in which the oncoming vehicle trajectory includes controlled lateral offsets or other deviations, or provide an analysis of gap-validity under such deviations.
  4. [§IV-E, Eq. (11)] The optimal-gap selection depends on weights w1–w4, the reversal discount factor, and the history factor alpha, but these are hand-chosen free parameters and no sensitivity analysis is provided. Since the paper claims decision robustness partly on the basis of this cost function, please include an ablation or sensitivity study showing that the selected gap and the overall pass rate are stable across reasonable variations of these weights.
  5. [§V-B, Eqs. (18)–(19)] The endpoint constraints for the cut-in and back-up strategies require the final waypoint to lie on or outside a circle of radius R around a corner point, but they do not guarantee that the entire path from the current state to that endpoint is feasible with the minimum turning radius R. The optimization uses only the finite-difference constraint H(pi, pi+1)=0, which, as noted above, does not bound curvature. Please demonstrate that the generated paths respect the kinematic limits, or add an explicit feasibility check and discuss what happens when the endpoint is unreachable.
minor comments (6)
  1. [Abstract and Introduction] The term 'passible' in the Introduction should be 'passable', and the acronym SM-NR is used without being defined in the abstract or the conclusion; please define it at first use in each section where it appears.
  2. [§IV-A, Eq. (3)] The quantities A and B are used in Eq. (3) before they are defined; please define them immediately before the display, or move their definitions earlier.
  3. [§IV-D, Fig. 4] The figure caption refers to colors (red, green, blue) but the printed version may not render colors distinctly; please add hatching or line styles so that the meeting gaps, non-meeting areas, and different boundaries are distinguishable in grayscale.
  4. [References] References [17] and [41] appear to be the same paper by Rösman, Hoffmann, and Bertram; please consolidate these citations.
  5. [§V-B, Eq. (15)] The inequality signs in the stationary-vehicle avoidance constraint should be checked against the coordinate convention of the SL frame, since the meaning of 'between' depends on which curve is the upper boundary; please clarify the sign convention.
  6. [§VI-B and §VI-C] The text refers to colors such as 'marked in red', 'marked in blue', and 'stark red' in Figs. 8 and 9, but the vehicle colors described in Sec. VI-A are orange, black, and green; please ensure the color references are consistent and visible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the geometric boundary construction and gap identification are self-contained derivations from stated kinematic and geometric inputs, and the simulations do not fit the model's parameters.

full rationale

The paper's central derivation, the expanded boundaries Lrear and Lmodel, is built from explicit geometric equations (Sec. IV-A through IV-C, Eqs. 1-10) that solve for detour endpoints using only the vehicle width W, length L, minimum turning radius R, road width Wr, and stationary-vehicle corner positions. No quantity needed to produce the claimed meeting-gap identification is fitted to the experimental success rates; the weights in the gap-selection cost (Eq. 11) are hand-chosen free parameters, which is a reproducibility/robustness concern rather than a circular step. The meeting-gap condition in Sec. IV-D is a definitional comparison of the two expanded boundaries, not a prediction that is equivalent to its inputs by construction. The homology-class criterion is cited from external prior work [17], and the trajectory optimization uses standard kinematic constraints and objective functions; the authors' own prior work is used only as a comparison baseline [4] and for a standard time-optimal objective [42], neither of which is load-bearing for the novel claim. The explicit assumption that both vehicles advance tightly alongside stationary vehicles and road edges is an idealization that limits external validity, and the quantitative replay data may conform to that behavior, but the paper does not fit the model to that data, so the high pass rates are not forced by the derivation. Overall, the derivation chain is self-contained and no specific circular reduction can be exhibited.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The method's core geometric boundary construction uses only standard geometry plus the vehicle's physical dimensions and turning radius, so it contributes no fitted physical constants. The main free parameters are the gap-selection cost weights, which are not specified. The load-bearing domain assumptions are straight roads and cooperative boundary-hugging behavior by both vehicles.

free parameters (3)
  • Gap-selection cost weights w1-w4 = Not reported
    Equ (11) defines the optimal gap as argmin of a weighted sum of gap length, selection count, distance, and lane direction. The weights are not specified and no sensitivity study is provided; the chosen gap depends directly on them.
  • Reversal discount factor = Not reported (stated as less than 1)
    Sec IV-E says the cost of reversing into a gap is multiplied by a discount factor below 1 to encourage safety, but the value is not given.
  • History factor alpha = 0.9
    Equ (11) uses alpha = 0.9 if the gap was selected at the last decision; the value is chosen heuristically.
assumptions (5)
  • domain assumption Road boundaries are straight, with Bup(x) = Wr/2 and Bdown(x) = -Wr/2.
    Assumed in Sec III; all boundary computations and meeting-gap definitions rely on a constant road width. Curved roads are listed as a limitation in Sec VIII.
  • ad hoc to paper Both the autonomous vehicle and the oncoming vehicle advance tightly alongside stationary vehicles and road edges, forming expanded boundaries.
    Stated in Sec IV before gap identification: 'By assuming the vehicles approaching from two ends advance tightly alongside the stationary vehicles and road edges'. If the oncoming vehicle does not behave this way, candidate meeting gaps may be invalid.
  • domain assumption Stationary vehicles can be represented as rectangles with at most three valid corner points, and detours around those corners are sufficient to build the boundary.
    Used throughout Sec IV-A to IV-C; the corner-point simplification ignores general shapes and assumes the vehicle can always follow the constructed arc-based detour.
  • domain assumption The oncoming vehicle's future motion is estimated at constant current speed via Kalman filter for meeting-point prediction.
    Sec IV-E assumes both vehicles 'maintain their speeds' to compute the meeting position x; reactive or braking behavior is not modeled in gap selection.
  • ad hoc to paper The optimization's finite-difference constraint H(pi, pi+1)=0 in Equ (14) is sufficient for kinodynamic feasibility.
    The paper claims kinematic constraints are satisfied, but no curvature bound is written; this assumption is implicit in the claim that optimized trajectories are executable.

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Pith. "Pith review of Scene Modeling of Autonomous Vehicles Avoiding Stationary and Moving Vehicles on Narrow Roads." pith.science (2026). https://pith.science/paper/CRGIG3SB

@misc{pith2026241213305,
  author       = {Pith},
  title        = {Pith review of: Scene Modeling of Autonomous Vehicles Avoiding Stationary and Moving Vehicles on Narrow Roads},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CRGIG3SB}},
  note         = {Machine review of arXiv:2412.13305}
}
read the original abstract

Navigating narrow roads with oncoming vehicles is a significant challenge that has garnered considerable public interest. These scenarios often involve sections that cannot accommodate two moving vehicles simultaneously due to the presence of stationary vehicles or limited road width. Autonomous vehicles must therefore profoundly comprehend their surroundings to identify passable areas and execute sophisticated maneuvers. To address this issue, this paper presents a comprehensive model for such an intricate scenario. The primary contribution is the principle of road width occupancy minimization, which models the narrow road problem and identifies candidate meeting gaps. Additionally, the concept of homology classes is introduced to help initialize and optimize candidate trajectories, while evaluation strategies are developed to select the optimal gap and most efficient trajectory. Qualitative and quantitative simulations demonstrate that the proposed approach, SM-NR, achieves high scene pass rates, efficient movement, and robust decisions. Experiments conducted in tiny gap scenarios and conflict scenarios reveal that the autonomous vehicle can robustly select meeting gaps and trajectories, compromising flexibly for safety while advancing bravely for efficiency.

Figures

Figures reproduced from arXiv: 2412.13305 by the authors.

Figure 1
Figure 1. Illustration of vehicles passing through the narrow road. The autonomous vehicle is expected to proactively pull over and wait at the red gap to avoid moving vehicles approaching from the oncoming direction. part of the road cannot allow two moving vehicles to meet, either due to the road’s limited width or the presence of sta￾tionary vehicles along the roads. When two vehicles approach from opposite directions on s… view at source ↗
Figure 2
Figure 2. Framework of our proposed method, SM-NR. Taking road information and vehicle distribution as input, boundaries are outlined, candidate meeting gaps are identified, and the optimal gap is selected. Candidate trajectories are then initialized and optimized, with the optimal one selected and executed. two expanded boundaries intersect, the whole road is divided into non-meeting areas and meeting gaps which provide enou… view at source ↗
Figure 3
Figure 3. Illustration of scene modeling for detouring a stationary vehicle. The autonomous vehicle is assumed to move closely alongside the road boundary and stationary vehicles. (a) A stationary vehicle is simplified as four corner points (pxi, pyi)|i={1,2,3,4}. To detour around a point (px1, py1), the autonomous vehicle starts to turn left at (x1, y1) to leave the road boundary and then turns right at (x2, y2) until it is … view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Illustration of idenfitying candidate meeting gaps. (a) An expanded boundary of the rear center of the autonomous vehicle, Lrear av , is obtained by connecting detours around stationary vehicles Lrear av = {p1,1 → p1,2} ∪ {p2,1 → p2,2} ∪ ...{p5,1 → p5,2}. Expanding Lre…
Figure 5
Figure 5. Figure 5: Illustration of the simplified model on the narrow road scenario. follow the lane direction clane. The gap with the lowest cost is selected as the optimal gap: g ∗ = arg min g∈G α(−w1clen − w2ctimes + w3cdis − w4clane) (11) where α is 0.9 if the gap was selected at the…
Figure 6
Figure 6. Figure 6: Illustration of 7 trajectories belonging to different homology classes. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Illustration of (a) the constraint of avoiding moving vehicles and (b) [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Illustration of the single normal meeting gap scenario. (a) When there [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Illustration of the robustness and efficiency in a conflict scenario with two meeting gaps. (a [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: Illustration of the tiny scenario. By modeling stationary vehicles as corner points, the expanded boundaries and strategies can be robustly identified, even in challenging situations such as (a1-a5) stationary vehicles parked in the wrong lane or (b1) stationary vehic…
Figure 11
Figure 11. Figure 11: Illustration of the meeting by oncoming lane direction. (a [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]
Figure 12
Figure 12. Figure 12: Performance of the proposed SM-NR in the tiny meeting gap scenario. (a) The SM-NR-controlled vehicle moves from the left end and passes [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: Performance of SM-NR in the conflict scenario with two meeting gaps. (a) There are two gaps between vehicles. In the order of decreasing speed [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]

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Reference graph

Works this paper leans on

44 extracted references · 41 canonical work pages

  1. [1]

    Uncertainty-aware decision-making for autonomous driving at uncon- trolled intersections,

    X. Tang, G. Zhong, S. Li, K. Yang, K. Shu, D. Cao, and X. Lin, “Uncertainty-aware decision-making for autonomous driving at uncon- trolled intersections,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9725–9735, 2023

    work page 2023

  2. [2]

    Milestones in autonomous driving and intelligent vehicles—part i: Control, computing system design, communication, hd map, testing, and human behaviors,

    L. Chen, Y . Li, C. Huang, Y . Xing, D. Tian, L. Li, Z. Hu, S. Teng, C. Lv, J. Wang, D. Cao, N. Zheng, and F.-Y . Wang, “Milestones in autonomous driving and intelligent vehicles—part i: Control, computing system design, communication, hd map, testing, and human behaviors,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 53, no. 9, pp. ...

    work page 2023

  3. [3]

    Deep learning in robotics: Survey on model structures and training strategies,

    A. I. K ´aroly, P. Galambos, J. Kuti, and I. J. Rudas, “Deep learning in robotics: Survey on model structures and training strategies,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 266–279, 2021

    work page 2021

  4. [4]

    P2eg: Prediction and planning integrated robust decision-making for automated vehicle negotiating in narrow lane with explorative game,

    Q. Zhang, X. Li, E. He, S. Ding, N. Wang, and J. Liu, “P2eg: Prediction and planning integrated robust decision-making for automated vehicle negotiating in narrow lane with explorative game,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 4819–4826

    work page 2022

  5. [5]

    A convex optimal control framework for autonomous vehicle intersection crossing,

    X. Pan, B. Chen, S. Timotheou, and S. A. Evangelou, “A convex optimal control framework for autonomous vehicle intersection crossing,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 1, pp. 163–177, 2023

    work page 2023

  6. [6]

    Robust decision making for autonomous vehicles at highway on-ramps: A constrained adversarial reinforcement learning approach,

    X. He, B. Lou, H. Yang, and C. Lv, “Robust decision making for autonomous vehicles at highway on-ramps: A constrained adversarial reinforcement learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 4103–4113, 2023

    work page 2023

  7. [7]

    Epsilon: An efficient planning system for automated vehicles in highly interactive environments,

    W. Ding, L. Zhang, J. Chen, and S. Shen, “Epsilon: An efficient planning system for automated vehicles in highly interactive environments,” IEEE Transactions on Robotics , vol. 38, no. 2, pp. 1118–1138, 2022

    work page 2022

  8. [8]

    Autonomous vehicles security: Challenges and solutions using blockchain and artificial intelligence,

    G. Bendiab, A. Hameurlaine, G. Germanos, N. Kolokotronis, and S. Shi- aeles, “Autonomous vehicles security: Challenges and solutions using blockchain and artificial intelligence,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 3614–3637, 2023

    work page 2023

Show all 44 references
  1. [9]

    Reinforce- ment learning based negotiation-aware motion planning of autonomous vehicles,

    Z. Wang, Y . Zhuang, Q. Gu, D. Chen, H. Zhang, and W. Liu, “Reinforce- ment learning based negotiation-aware motion planning of autonomous vehicles,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 4532–4537

    2021

  2. [10]

    Multi-agent path integral control for interaction-aware motion planning in urban canals,

    L. Streichenberg, E. Trevisan, J. J. Chung, R. Siegwart, and J. Alonso- Mora, “Multi-agent path integral control for interaction-aware motion planning in urban canals,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 1379–1385

    2023

  3. [11]

    Modeling the pedestrian flow before bottleneck through learning-based method,

    N. Jiang, L. Yang, R. K. K. Yuen, and C. Zhai, “Modeling the pedestrian flow before bottleneck through learning-based method,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 7, pp. 7035–7047, 2023

    2023

  4. [12]

    Viewing robot navigation in human environment as a cooperative activity,

    H. Khambhaita and R. Alami, “Viewing robot navigation in human environment as a cooperative activity,” in Robotics Research , N. M. Amato, G. Hager, S. Thomas, and M. Torres-Torriti, Eds. Cham: Springer International Publishing, 2020, pp. 285–300

    2020

  5. [13]

    Human- centered x–y–t space path planning for mobile robot in dynamic envi- ronments,

    I. Nishitani, T. Matsumura, M. Ozawa, and M. Takahashi, “Human- centered x–y–t space path planning for mobile robot in dynamic envi- ronments,” Robotics and Autonomous Systems, vol. 66, pp. 18–26, 2015

    2015

  6. [14]

    Comet: Modeling group cohesion for socially compliant robot navigation in crowded scenes,

    A. J. Sathyamoorthy, U. Patel, M. Paul, N. K. S. Kumar, Y . Savle, and D. Manocha, “Comet: Modeling group cohesion for socially compliant robot navigation in crowded scenes,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1008–1015, 2022

    2022

  7. [15]

    Chassis global dynamics-oriented trajectory planning for automated vehicles,

    S. Cheng, H.-N. Peng, X.-X. Dong, Z.-J. Liu, and L. Li, “Chassis global dynamics-oriented trajectory planning for automated vehicles,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 950–959, 2024

    2024

  8. [16]

    A dynamic evolution method for autonomous vehicle groups in an urban scene,

    G. Yuan, J. Cheng, M. Zhou, S. Cheng, S. Gao, C. Jiang, and A. Abu- sorrah, “A dynamic evolution method for autonomous vehicle groups in an urban scene,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 6, pp. 3450–3460, 2023

    2023

  9. [17]

    Integrated online trajec- tory planning and optimization in distinctive topologies,

    C. R ¨osmann, F. Hoffmann, and T. Bertram, “Integrated online trajec- tory planning and optimization in distinctive topologies,” Robotics and Autonomous Systems, vol. 88, 11 2016

    2016

  10. [18]

    Nomad: Goal masked diffusion policies for navigation and exploration,

    A. Sridhar, D. Shah, C. Glossop, and S. Levine, “Nomad: Goal masked diffusion policies for navigation and exploration,” in 2024 IEEE Inter- national Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 63–70

    2024

  11. [19]

    Vectornet: Encoding hd maps and agent dynamics from vectorized rep- resentation,

    J. Gao, C. Sun, H. Zhao, Y . Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding hd maps and agent dynamics from vectorized rep- resentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 525–11 533

    2020

  12. [20]

    Marc: Multipolicy and risk- aware contingency planning for autonomous driving,

    T. Li, L. Zhang, S. Liu, and S. Shen, “Marc: Multipolicy and risk- aware contingency planning for autonomous driving,” IEEE Robotics and Automation Letters , 2023

    2023

  13. [21]

    nuplan: A closed-loop ml- based planning benchmark for autonomous vehicles,

    H. Caesar, J. Kabzan, K. S. Tan, W. K. Fong, E. Wolff, A. Lang, L. Fletcher, O. Beijbom, and S. Omari, “nuplan: A closed-loop ml- based planning benchmark for autonomous vehicles,” arXiv preprint arXiv:2106.11810, 2021

    2021 arXiv

  14. [22]

    Human-centered robot navi- gation—towards a harmoniously human-robot coexisting environment,

    C.-P. Lam, C.-T. Chou, and L.-C. Fu, “Human-centered robot navi- gation—towards a harmoniously human-robot coexisting environment,” IEEE Transactions on Robotics , vol. 27, no. 1, pp. 99–112, 2011

    2011

  15. [23]

    A survey on motion prediction and risk assessment for intelligent vehicles,

    S. Lefevre, D. Vasquez, and C. Laugier, “A survey on motion prediction and risk assessment for intelligent vehicles,” Robomech Journal, vol. 1, 07 2014

    2014

  16. [24]

    Assessing the social criteria for human- robot collaborative navigation: A comparison of human-aware naviga- tion planners,

    H. Khambhaita and R. Alami, “Assessing the social criteria for human- robot collaborative navigation: A comparison of human-aware naviga- tion planners,” in 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) , 2017, pp. 1140– 1145

    2017

  17. [25]

    Hateb-2: Reactive planning and decision making in human-robot co-navigation,

    P. Teja S. and R. Alami, “Hateb-2: Reactive planning and decision making in human-robot co-navigation,” in 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO- MAN), 2020, pp. 179–186

    2020

  18. [26]

    Efficient and trustworthy social navigation via explicit and implicit robot–human communication,

    Y . Che, A. Okamura, and D. Sadigh, “Efficient and trustworthy social navigation via explicit and implicit robot–human communication,” IEEE Transactions on Robotics , vol. PP, pp. 1–16, 01 2020

    2020

  19. [27]

    The combinatorial aspect of motion planning: Maneuver variants in structured environments,

    P. Bender, O. S. Tas, J. Ziegler, and C. Stiller, “The combinatorial aspect of motion planning: Maneuver variants in structured environments,” in 2015 IEEE Intelligent Vehicles Symposium (IV) , 2015, pp. 1386–1392

    2015

  20. [28]

    Time dependent plan- ning on a layered social cost map for human-aware robot navigation,

    M. Kollmitz, K. Hsiao, J. Gaa, and W. Burgard, “Time dependent plan- ning on a layered social cost map for human-aware robot navigation,” in 2015 European Conference on Mobile Robots (ECMR) , 2015, pp. 1–6

    2015

  21. [29]

    Reactive, proactive, and inducible proximal crowd robot navigation method based on inducible social force model,

    M. Kamezaki, Y . Tsuburaya, T. Kanada, M. Hirayama, and S. Sugano, “Reactive, proactive, and inducible proximal crowd robot navigation method based on inducible social force model,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3922–3929, 2022

    2022

  22. [30]

    An optimization-based motion planner for car-like logistics robots on narrow roads,

    L. Yu, H. Wu, and H. Jiao, “An optimization-based motion planner for car-like logistics robots on narrow roads,” Sensors, vol. 22, no. 22, 2022

    2022

  23. [31]

    Learning to robustly negotiate bi-directional lane usage in high-conflict driving scenarios,

    C. Killing, A. Villaflor, and J. M. Dolan, “Learning to robustly negotiate bi-directional lane usage in high-conflict driving scenarios,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 8090–8096

    2021

  24. [32]

    Strategy: An introduction to game theory,

    J. Watson, “Strategy: An introduction to game theory,” Journal of Economic Theory, 2013

    2013

  25. [33]

    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,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 8, pp. 8014–8027, 2023

    2023

  26. [34]

    Prediction based trajec- tory planning for safe interactions between autonomous vehicles and moving pedestrians in shared spaces,

    B. Yang, S. Yan, Z. Wang, and K. Nakano, “Prediction based trajec- tory planning for safe interactions between autonomous vehicles and moving pedestrians in shared spaces,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 10, pp. 10 513–10 524, 2023

    2023

  27. [35]

    Fac- torization of dynamic games over spatio-temporal resources,

    A. Zanardi, S. Bolognani, A. Censi, F. Dorfler, and E. Frazzoli, “Fac- torization of dynamic games over spatio-temporal resources,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 13 159–13 166

    2022

  28. [36]

    Game-theoretic planning for autonomous driving among risk-aware human drivers,

    R. Chandra, M. Wang, M. Schwager, and D. Manocha, “Game-theoretic planning for autonomous driving among risk-aware human drivers,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 2876–2883

    2022

  29. [37]

    Hierar- chical motion planning for autonomous driving in large-scale complex scenarios,

    S. Zhang, Z. Jian, X. Deng, S. Chen, Z. Nan, and N. Zheng, “Hierar- chical motion planning for autonomous driving in large-scale complex scenarios,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 8, pp. 13 291–13 305, 2022

    2022

  30. [38]

    Unfreezing the robot: Navigation in dense, interacting crowds,

    P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2010, pp. 797–803

    2010

  31. [39]

    Sur les courbes `a double courbure,

    F. Frenet, “Sur les courbes `a double courbure,” Journal de math´ematiques pures et appliqu ´ees, vol. 17, pp. 437–447, 1852

  32. [40]

    The frenet frame as a generalization of the park transform,

    F. Milano, “The frenet frame as a generalization of the park transform,” IEEE Transactions on Circuits and Systems I: Regular Papers , vol. 70, no. 2, pp. 966–976, 2022

    2022

  33. [41]

    Integrated online trajec- tory planning and optimization in distinctive topologies,

    C. R ¨osmann, F. Hoffmann, and T. Bertram, “Integrated online trajec- tory planning and optimization in distinctive topologies,” Robotics and Autonomous Systems, vol. 88, 11 2016. 15

    2016

  34. [42]

    Improve computing efficiency and motion safety by analyzing environment with graphics,

    Q. Zhang, S. Wu, Y . Jia, Y . Xu, and J. Liu, “Improve computing efficiency and motion safety by analyzing environment with graphics,” IEEE Transactions on Automation Science and Engineering , pp. 1–14, 2023

    2023

  35. [43]

    Kinodynamic trajectory optimization and control for car-like robots,

    C. R ¨osmann, F. Hoffmann, and T. Bertram, “Kinodynamic trajectory optimization and control for car-like robots,” in 2017 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS) , 2017, pp. 5681–5686

    2017

  36. [44]

    Efficient constrained path planning via search in state lattices,

    M. Pivtoraiko and A. Kelly, “Efficient constrained path planning via search in state lattices,” in International Symposium on Artificial Intel- ligence, Robotics, and Automation in Space . Munich Germany, 2005, pp. 1–7

    2005

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.