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REVIEW 4 major objections 7 minor 30 references

High-Speed Cornering Control and Real-Vehicle Deployment for Autonomous Electric Vehicles

T0 review · 4 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A simulation-trained RL policy can drive a consumer electric vehicle through real drift U-turns and wet right-angle turns when a two-step linear MPC handles the trajectory tracking.

desk verdict Real-vehicle drift demo is real, but the RL contribution collapses to Bézier tracking on the paper's own numbers. read the letter →

arxiv 2411.11762 v2 pith:RZO7RSZD submitted 2024-11-18 cs.RO cs.SYeess.SY

Shiyue Zhao , Junzhi Zhang , Neda Masoud , Yuhong Jiang , Heye Huang , Tao Liu This is my paper · ORCID
classification cs.ROcs.SYeess.SY
keywords autonomousdrivingdriftcorneringreinforcementlearningTD3modelpredictivecontrolsim-to-realtransfertrajectoryoptimizationelectricvehicledynamics
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 tackles the gap between simulation-trained reinforcement-learning controllers and real vehicles, a gap that has kept drift cornering research mostly in simulation. It proposes an RL-MPC fusion in which a TD3 policy trained in a simulated environment produces the primary control actions, while a two-step model predictive controller with a linear tire model generates corrective inputs so the vehicle tracks the trajectory the TD3 policy produces. The paper reports the first deployment of a scenario-to-action RL-based transient drift cornering algorithm on consumer-grade electric vehicles. In real-vehicle tests the fused controller completed a U-turn at a maximum speed of 10.4 m/s with a maximum side-slip angle of 63.7 degrees, and a right-angle drift turn on wet asphalt. If these results hold, simulation-trained extreme-maneuver controllers can be moved to real vehicles without retraining, shortening the path to time-optimal autonomous cornering.

What carries the argument

The load-bearing mechanism is the fusion of three components. First, a Bézier-based pre-trajectory optimizer solves, in Frenet coordinates, for a minimum-curvature path $l(s)$ and a curvature-limited speed plan; that pre-optimized trajectory defines the immediate reward that makes TD3 training feasible despite a sparse terminal time reward. Second, the trained TD3 policy, run offline on the virtual curve matched to the real test site, produces the preview trajectory $T_p$ in Cartesian coordinates; this trajectory carries the optimal-maneuver information. Third, a two-step model predictive controller built on a linearized three-degree-of-freedom vehicle model generates corrective inputs $\Delta u_{MPC}$ that are added to the RL actions, with a safety fallback that applies moderate braking when the side-slip angle exceeds a threshold. The MPC's role is not to plan the drift but to hold the vehicle on the RL-produced trajectory, which is why the authors can use a linear tire model in a regime where the dynamics are strongly nonlinear.

What would settle it

Run the same U-turn test on a surface with measured peak friction near 0.55: the paper's own parameter sweep predicts only partial completion (127 of 180 degrees), so a full completion would contradict the claimed robustness boundary, while tracking failure with the MPC correction saturated would show that the linear correction cannot cover large model mismatches.

Watch

Extended reading notes

Core claim

The paper's central claim is that a simulated RL policy can be made to control a real vehicle in a highly nonlinear drift regime by splitting its role in two. Offline, the trained TD3 policy is run on a matched virtual curve to produce a preview trajectory; this trajectory is the reference, and it is deliberately different from the Bézier pre-optimized path used during training. Online, the policy's actions are still sent to the vehicle as primary inputs, but a two-step MPC with a linear tire model measures tracking error against the preview trajectory and adds corrective inputs in steering and longitudinal acceleration, converted to torque and brake pressure. The fused input is $u_t = A_{RL} + \Delta u_{MPC}$. On a consumer-grade rear-wheel-drive electric vehicle, this controller completed a 180-degree drift U-turn in 4.8 s with a maximum speed of 10.4 m/s and a maximum center-of-mass side-slip angle of 63.7 degrees, and completed a 90-degree right-angle drift turn on a wet road in 3.19 s, while the RL-only policy failed the U-turn and the MPC-only policy could not stabilize it.

Load-bearing premise

The load-bearing assumption is that simulating the trained TD3 policy on a matched virtual curve yields a preview trajectory close enough to the real optimum, and that the two-step linear-tire MPC can correct the real vehicle's deviations well enough without invoking the full nonlinear dynamics.

Editorial extensions

If this is right

  • Sim-to-real transfer for transient drift control can be achieved without domain randomization or on-vehicle retraining; the MPC correction absorbs a meaningful amount of model mismatch.
  • Consumer-grade electric vehicles, not specialized research platforms, become capable of executing high-side-slip cornering maneuvers, changing what production stability control could attempt.
  • The U-turn and right-angle turn were completed in 4.81 s and 3.19 s respectively, close to the simulated RL baselines of 4.52 s and 2.81 s, so the fusion preserves most of the speed advantage of the learned policy.
  • The controller degrades gracefully as the adhesion coefficient in the training environment drops from 0.95 to 0.65, with cornering time increasing but the task still completed; at 0.55 the maneuver is only partially completed, defining a practical operating envelope.

Reading between the lines

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

  • Because the TD3 policy is reduced to a reference generator, the same RL-MPC wrapper could in principle deploy other simulation-trained maneuver policies, such as obstacle-avoidance or recovery maneuvers, without retraining the wrapper.
  • The virtual-curve matching step is currently offline and scenario-specific; a natural extension is online curve identification, which would let the preview trajectory be regenerated as the vehicle approaches an unseen corner.
  • The robustness sweep varies friction inside the training environment, not on the real road; directly measuring real surface friction and watching whether the MPC correction grows in proportion would quantify how much of the claimed gap-bridging is due to the fusion.
  • The failure at 0.55 suggests that a friction-estimation module, rather than a fixed corrective MPC, would be the next step if the controller is to operate safely near the edge of the traction envelope.
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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 / 7 minor

Summary. This paper presents a control framework for high-speed drift cornering of autonomous electric vehicles, combining a TD3 reinforcement-learning (RL) controller trained in simulation with a two-step linear-tire MPC that provides corrective inputs during real-vehicle deployment. A Bézier-based pre-trajectory optimizer shapes the RL reward and generates an initial speed plan. In deployment, the trained policy is simulated on a matched virtual curve to produce a preview trajectory, which the MPC then tracks while the RL actions serve as feedforward inputs. The approach is validated on a consumer-grade electric vehicle performing a drift U-turn and a right-angle turn on wet road, with maximum speeds of 10.4 m/s and 10.6 m/s and maximum side-slip angles of 63.7° and 46.1°, respectively. The authors claim this is the first deployment of a scenario-to-action RL-based transient drift cornering algorithm on a consumer electric vehicle.

Significance. If the central claims hold, the paper makes a notable empirical contribution: it demonstrates that an RL-trained drift controller can be transferred to a full-scale consumer EV with the aid of an MPC wrapper, and it provides real-vehicle data and video evidence. The comparison against RL-alone and MPC-alone baselines is a useful design study. However, the significance is weakened by two issues: (i) the trained RL policy's simulation times exactly match the Bézier pre-trajectory times, suggesting that the RL does not in fact optimize cornering time as claimed; and (ii) the preview trajectory is generated by the policy itself, making the deployment evaluation to some extent self-referential. The paper would be strengthened by showing that RL produces a strictly faster or qualitatively different trajectory than the pre-optimized one, and by providing repeated trials and standard statistical measures.

major comments (4)
  1. [§III.A and Tables I, III, IV] The reported simulation cornering times of the trained TD3 policy (2.81 s for the 90° turn in Table IV, 4.52 s for the U-turn in Table III) are exactly equal, to two decimal places, to the Bézier pre-trajectory times in Table I. This equality strongly suggests that the RL policy has collapsed to tracking the pre-optimized Bézier path at the planned speed, rather than discovering a time-optimal transient drift line. If this is the case, the central contribution "TD3-based controller ... capable of performing transient drift cornering aimed at optimizing cornering time" is not demonstrated, and the preview trajectory T_p in Eq. (18) is not an independent optimum but a re-issuance of the Bézier trajectory. Please provide the reward weights (k_pl, k_pv, k_s, k_s1, k_t1, k_t2), training reward curves, and a side-by-side comparison of the RL policy's trajectory and speed profile against the Bézier pre-trajectory. At minimum, show that the RL policy achieves a strictly lower cornering time or a materially different (e.g., higher side-slip) trajectory in simulation.
  2. [§IV.A] The preview trajectory T_p is generated by simulating the trained TD3 policy on the matched virtual curve (Eq. 18), and the MPC then tracks this trajectory. This makes the reference trajectory endogenous: the real-vehicle success is evaluated against the controller's own simulated output, not against an independently derived optimal line or a ground-truth minimum-time path. The claim that the vehicle tracks the "optimal trajectory" is therefore circular in the absence of external validation. Please compare against an independent time-optimal trajectory (e.g., from a separate optimization method) or revise the language to "policy-generated trajectory" and provide evidence that this trajectory is near-optimal with respect to cornering time in simulation.
  3. [§V.B and V.C, Tables III and IV] Each evaluation in Tables III and IV reports a single run per policy. Drift maneuvers are highly sensitive to initial conditions, tire temperature, surface variations, and sensor noise; without repeated runs and error bars (or at least a demonstration of trial-to-trial consistency), the comparison between the proposed policy and the baselines is not statistically grounded. In addition, the baselines are not fully specified: the MPC-only policy is cited to [30] but the exact weights, horizons, and linear tire parameters used here are not given, and the RL-only policy's network architecture and exploration noise during the real-vehicle test are not described. Please add repetition counts, variance or range statistics, and complete baseline parameter settings to make the comparison reproducible.
  4. [§V.D] The robustness analysis in Table V varies the adhesion coefficient in the RL training environment, not in the real vehicle or in the MPC's model. This tests the sensitivity of the simulated policy to friction mismatch, but it does not directly support the claim that the real-vehicle controller is robust to real-world variations, since the MPC correction loop in deployment is not part of this experiment. Please clarify the scope of this robustness test and supplement it with real-world runs on different surfaces (the wet right-angle test already provides some evidence) or explicitly discuss how the MPC correction is expected to handle friction deviations beyond the training envelope.
minor comments (7)
  1. [Eq. (12) and surrounding text] The description of the constants k_t1 and k_t2 is inconsistent: the text first says "The constant k_t1 is a positive value" and then later says "The constant k_t1, on the other hand, is a positive value that incentivizes achieving the shortest possible extreme cornering time." Please clarify the distinct roles of k_t1 and k_t2 in the terminal reward.
  2. [Eq. (25) and following] The matrices A_t and B_t in Eq. (25) are not defined; the statement that their determination is "well-established and will not be detailed here" leaves the reader unable to reproduce the MPC. Please provide the explicit matrices or a reference that includes them.
  3. [Eqs. (27) and (28)] Equations (27) and (28) appear to have formatting issues: the state vector definitions and the integration limits are unclear (e.g., the term "260" in the matrix exponential). Please revise these equations for readability and correctness.
  4. [§IV] The phrase "We believes that this integration enhances" should read "We believe that this integration enhances."
  5. [Abstract] In the abstract, there is a double comma in "fusion mechanism, , where" that should be corrected to "fusion mechanism, where."
  6. [Tables III and IV] The paper uses "Task Completion (deg)" as an evaluation metric but does not define how this angle is measured (e.g., accumulated heading change, reached checkpoint angle, or final vehicle heading relative to the exit). Please define the metric precisely in the text.
  7. [Figures 9 and 11] The real-vehicle state plots would be more informative if annotated with the start and end of the corner, the period of the safety fallback (if any), and the side-slip threshold used for fallback activation.

Circularity Check

2 steps flagged · score 4.0 of 10

The 'optimal' preview trajectory is defined as the trained policy's own simulation and then used as the MPC reference; reported RL simulator times exactly match the Bezier baseline, so the claimed RL time-optimality is self-referential rather than independently derived.

  1. self definitional [Section IV.A, Eq. (18); Section IV.B]
    "This generates a preview trajectory T_p, which outlines the optimal path for the vehicle to follow. It is important to note that this preview trajectory differs from the pre-optimized trajectory used earlier in training. ... the trajectory used to generate the corrective input is not the pre-optimized trajectory obtained in Section III.A, but rather the trajectory produced by the RL agent during execution in the same simulation scenario."

    T_p is defined by Eq. (18) as the output of simulating the trained TD3 policy, and the MPC cost in Eq. (32) then penalizes deviation from T_p. The 'optimal path' is therefore the policy's own closed-loop simulation; the accompanying statement that 'the RL inputs are designed to reflect global optimality' is an assertion about that same policy output, not an independently derived optimum. Real-vehicle success shows that the fusion controller can track the policy's self-generated trajectory, but the optimality claim is self-referential because the reference trajectory is constructed from the very controller being evaluated.

  2. fitted input called prediction [Eq. (12), Table I, Tables III and IV]
    "the terminal reward r_t ... is based on the total time required to complete the track ... t_ref is the total driving time of the pre-optimized trajectory. ... 90-degree turn 2.94 2.81 ... U-turn 4.96 4.52 ... RL policy in simulators 180/180 11.0 67.4 4.52 ... RL policy in simulators 90/90 11.2 52.6 2.81"

    The TD3 policy is trained with a terminal reward anchored to the Bezier pre-trajectory time t_ref (Eq. 12) and an immediate reward r_p that rewards tracking the pre-optimized path at the planned speed. The reported RL-in-simulator cornering times equal the Bezier optimization times to two decimal places (2.81 s for the 90-degree turn, 4.52 s for the U-turn). Thus the claimed 'RL-based transient drift cornering aimed at optimizing cornering time' prediction is, in the reported data, the pre-trajectory time re-issued by the policy; no faster-than-Bezier trajectory is demonstrated, so the central time-optimality result reduces to the reward-shaped input rather than an independent RL optimization.

full rationale

The tracking architecture itself is not circular in a control sense: Eq. (18) defines the preview trajectory as the TD3 policy's simulation, and Eq. (32) tracks that trajectory with MPC; the real-vehicle U-turn and right-angle tests provide external evidence that the fused controller can follow this reference. However, the paper's optimality claim is self-referential: T_p is by definition the policy's own output, and the policy was trained with rewards anchored to the Bezier pre-trajectory (Eq. 12). The exact agreement of Tables I, III, and IV (2.81 s and 4.52 s) shows that the RL simulator 'prediction' coincides with the Bezier baseline, so the central claim that RL optimizes cornering time is not independently supported. This is partial circularity rather than full circularity: the deployment success is externally grounded, and there is no load-bearing self-citation or uniqueness theorem chain. The score reflects the self-referential optimality claim and the reward-anchored prediction, while acknowledging the genuine real-vehicle validation.

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

The central claim rests on the fidelity of the CarSim simulation for generating a reference trajectory, on the adequacy of a linear tire model for MPC corrections in a highly nonlinear regime, and on hand-tuned reward and cost weights whose values are not disclosed.

free parameters (3)
  • Reward weights k_pl, k_pv, k_s, k_s1, k_t1, k_t2 = not specified
    Hand-tuned constants in the reward function (Section III.B) that shape trajectory tracking, side-slip, and terminal time. The paper does not list their values or a tuning procedure.
  • MPC weight matrices Q and R = not specified
    Cost function weights in Eq. (32) for state error and input rate; values are not reported.
  • RL training hyperparameters = not specified
    Learning rates, discount factor, policy delay, exploration noise, network sizes, and number of training steps are not provided in Table II or the text.
assumptions (3)
  • domain assumption The CarSim model captures the real vehicle's drift dynamics with sufficient fidelity to produce a valid preview trajectory.
    Section IV.A uses the TD3 policy in CarSim to generate the preview trajectory T_p that the real vehicle must follow.
  • domain assumption A linear tire model is adequate for computing corrective MPC inputs in a highly nonlinear drift regime.
    Section IV.B states that a linear tire model [29] is used to compute corrective inputs, despite side-slip angles above 60 degrees.
  • domain assumption The virtual curve V_c can be matched to the real curve closely enough that the simulated preview trajectory is representative.
    Section IV.A describes matching the virtual curve from the scene library with the actual curve environment.

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Cite this review

Pith. "Pith review of High-Speed Cornering Control and Real-Vehicle Deployment for Autonomous Electric Vehicles." pith.science (2026). https://pith.science/paper/RZO7RSZD

@misc{pith2026241111762,
  author       = {Pith},
  title        = {Pith review of: High-Speed Cornering Control and Real-Vehicle Deployment for Autonomous Electric Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RZO7RSZD}},
  note         = {Machine review of arXiv:2411.11762}
}
read the original abstract

Executing drift maneuvers during high-speed cornering presents significant challenges for autonomous vehicles, yet offers the potential to minimize turning time and enhance driving dynamics. While reinforcement learning (RL) has shown promising results in simulated environments, discrepancies between simulations and real-world conditions have limited its practical deployment. This study introduces an innovative control framework that integrates trajectory optimization with drift maneuvers, aiming to improve the algorithm's adaptability for real-vehicle implementation. We leveraged Bezier-based pre-trajectory optimization to enhance rewards and optimize the controller through Twin Delayed Deep Deterministic Policy Gradient (TD3) in a simulated environment. For real-world deployment, we implement a hybrid RL-MPC fusion mechanism, , where TD3-derived maneuvers serve as primary inputs for a Model Predictive Controller (MPC). This integration enables precise real-time tracking of the optimal trajectory, with MPC providing corrective inputs to bridge the gap between simulation and reality. The efficacy of this method is validated through real-vehicle tests on consumer-grade electric vehicles, focusing on drift U-turns and drift right-angle turns. The control outcomes of these real-vehicle tests are thoroughly documented in the paper, supported by supplementary video evidence (https://youtu.be/5wp67FcpfL8). Notably, this study is the first to deploy and apply an RL-based transient drift cornering algorithm on consumer-grade electric vehicles.

Figures

Figures reproduced from arXiv: 2411.11762 by the authors.

Figure 1
Figure 1. Drift Cornering Controller Design and Real Vehicle Deployment Process Overview. The left side outlines the controller design, featuring trajectory optimization with Bezier curves, velocity planning, and reward-influenced controllers via reinforcement learning. on scenario-to-action reinforcement learning (RL), which involves transient drift control with continuous state changes during cornering. However, RL-based co… view at source ↗
Figure 10
Figure 10. Trajectory comparison of different control strategies. Simulink environment on a host computer, then compiled and deployed to the Speedgoat for real-time operation. The Speedgoat is a portable real-time machine equipped with an Intel i7 2.5 GHz dual-core CPU and supports CAN FD communication. Based on the complexity of the vehicle state and control algorithm, a control task frequency of 10 ms (100 Hz) was selected. … view at source ↗

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Works this paper leans on

30 extracted references · 30 canonical work pages

  1. [19]

    Sim -to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting,

    S. H. Tó th, Z. J. Viharos , Á. Bá rdos, and Z. Szalay, "Sim -to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting," Vehicles, vol. 6, no. 2, pp. 781-798, 2024

    work page 2024

  2. [30]

    Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback ,

    D. Chu, H. Li, C. Zhao, T. Zhou, " Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback ," IEEE Trans. Intell. Transp. Syst., vol. 24, no. 2, pp. 2239-2250, 2023. Shiyue Zhao is currently a visiting scholar at the University of Michigan, Ann Arbor, USA. He received the B.S. degree in traffic engineering from Centra...

    work page 2023

  3. [1]

    Cooperative automated vehicles: A review of opportunities and challenges in socially intelligent vehicles beyond networking,

    S. W. Loke, "Cooperative automated vehicles: A review of opportunities and challenges in socially intelligent vehicles beyond networking," IEEE Trans. Intell. Veh., vol. 4, no. 4, pp. 509-518, 2019

    work page 2019

  4. [2]

    Collision -free emergency planning and control methods for CAVs considering intentions of surrounding vehicles,

    S. Zhao, J. Zhang, C. He, M. Huang, Y. Ji, and W. Liu, "Collision -free emergency planning and control methods for CAVs considering intentions of surrounding vehicles," ISA Trans., vol. 136, pp. 535 -547, 2023

    work page 2023

  5. [3]

    Autonomous vehicles on the edge: A survey on autonomous vehicle racing,

    J. Betz et al., "Autonomous vehicles on the edge: A survey on autonomous vehicle racing," IEEE Open J. Intell. Transp. Syst. , vol. 3, pp. 458 -488, 2022

    work page 2022

  6. [4]

    Online learning -informed feedforward -feedback controller synthesis for path tracking of autonomous vehicles,

    H. Chen and C. Lv, "Online learning -informed feedforward -feedback controller synthesis for path tracking of autonomous vehicles," IEEE Trans. Intell. Veh., 2022. 11

    work page 2022

  7. [5]

    Analysis and control of high sideslip manoeuvres,

    C. Voser, R. Y. Hindiyeh, and J. C. Gerdes, "Analysis and control of high sideslip manoeuvres," Veh. Syst. Dyn., vol. 48, no. S1, pp. 317-336, 2010

    work page 2010

  8. [6]

    Opening new dimensions: Vehicle motion planning and control using brakes while drifting,

    T. Goel, J. Y. Goh, and J. C. Gerdes, "Opening new dimensions: Vehicle motion planning and control using brakes while drifting," in Proc. IEEE Intell. Vehicles Symp. (IV), 2020, pp. 560-565

    work page 2020

Show all 30 references
  1. [7]

    Beyond the stable handling limits: nonlinear model predictive control for highly transient autonomous drifting,

    J. Y. Goh, M. Thompson, J. Dallas, and A. Balachandran, "Beyond the stable handling limits: nonlinear model predictive control for highly transient autonomous drifting," Veh. Syst. Dyn., pp. 1-24, 2024

    2024

  2. [8]

    High -speed autonomous drifting with deep reinforcement learning,

    P. Cai, X. Mei, L. Tai, Y. Sun, and M. Liu, "High -speed autonomous drifting with deep reinforcement learning," IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 1247-1254, 2020

    2020

  3. [9]

    Autonomous drift controller for distributed drive electric vehicle with input coupling and uncertain disturbance,

    X. Hou, J. Zhang, Y. Ji, W. Liu, and C. He, "Autonomous drift controller for distributed drive electric vehicle with input coupling and uncertain disturbance," ISA trans., vol. 120, pp. 1-17, 2022

    2022

  4. [10]

    Segment drift control with a supervision mechanism for autonomous vehicles,

    M. Liu, B. Leng, L. Xiong, Y. Yu, and X. Yang, "Segment drift control with a supervision mechanism for autonomous vehicles," in Actuators, 2021, vol. 10, no. 9, p. 219: MDPI

    2021

  5. [11]

    Dynamic drifting control for general path tracking of autonomous vehicles,

    G. Chen, X. Zhao, Z. Gao, and M. Hua, "Dynamic drifting control for general path tracking of autonomous vehicles," IEEE Trans. Intell. Veh., vol. 8, no. 3, pp. 2527-2537, 2023

    2023

  6. [12]

    Deep learning- based trajectory planning and control for autonomous ground vehicle parking maneuver,

    R. Chai, D. Liu, T. Liu, A. Tsourdos, Y. Xia, and S. Chai, "Deep learning- based trajectory planning and control for autonomous ground vehicle parking maneuver," IEEE Trans. Autom. Sci. Eng. , vol. 20, no. 3, pp. 1633-1647, 2023

    2023

  7. [13]

    Introduction to Reinforcement Learning

    S. E. Li, “Introduction to Reinforcement Learning ”, in Reinforcement learning for sequential decision and optimal control . Springer, 2023, p. 37

    2023

  8. [14]

    A hierarchical deep reinforcement learning framework with high efficiency and generalization for fast and safe navigation,

    W. Zhu and M. Hayashibe, "A hierarchical deep reinforcement learning framework with high efficiency and generalization for fast and safe navigation," IEEE Trans. Ind. Electron. , vol. 70, no. 5, pp. 4962 -4971, 2022

    2022

  9. [15]

    Simultaneous stabilization and tracking of basic automobile drifting trajectories,

    J. Y. Goh and J. C. Gerdes, "Simultaneous stabilization and tracking of basic automobile drifting trajectories," in Proc. IEEE Intell. Vehicles Symp. (IV), 2016, pp. 597-602: IEEE

    2016

  10. [16]

    Modeling and Control for Dynamic Drifting Trajectories,

    T. P. Weber and J. C. Gerdes, "Modeling and Control for Dynamic Drifting Trajectories," IEEE Trans. Intell. Veh., 2023

    2023

  11. [17]

    Drift control for cornering maneuver of autonomous vehicles,

    F. Zhang, J. Gonzales, S. E. Li, F. Borrelli, and K. Li, "Drift control for cornering maneuver of autonomous vehicles," Mechatronics, vol. 54, pp. 167-174, 2018

    2018

  12. [18]

    Adaptive Drift Control of Autonomous Electric Vehicles After Brake System Failures,

    S. Zhao, J. Zhang, C. He, X. Hou, and H. Huang, "Adaptive Drift Control of Autonomous Electric Vehicles After Brake System Failures," IEEE Trans. Ind. Electron., vol. 71, no. 6, pp. 6041-6052, 2024

    2024

  13. [20]

    Trajectory tracking control of an autonomous underwater vehicle using Lyapunov-based model predictive control,

    C. Shen, Y. Shi, and B. Buckham, "Trajectory tracking control of an autonomous underwater vehicle using Lyapunov-based model predictive control," IEEE Trans. Ind. Electron., vol. 65, no. 7, pp. 5796-5805, 2017

    2017

  14. [21]

    Autonomous driving at the handling limit using residual reinforcement learning,

    X. Hou, J. Zhang, C. He, Y. Ji, J. Zhang, and J. Han, "Autonomous driving at the handling limit using residual reinforcement learning," Adv. Eng. Inform., vol. 54, p. 101754, 2022

    2022

  15. [22]

    Self-learning drift control of automated vehicles beyond handling limit after rear -end collision,

    Y. Yin, S. E. Li, K. Li, J. Yang, and F. Ma, "Self-learning drift control of automated vehicles beyond handling limit after rear -end collision," Transp. Saf. Environ., vol. 2, no. 2, pp. 97-105, 2020

    2020

  16. [23]

    Design and implementation of deep neural network -based control for automatic parking maneuver process,

    R. Chai, A. Tsourdos, A. Savvaris, S. Chai, Y. Xia, and C. P. Chen, "Design and implementation of deep neural network -based control for automatic parking maneuver process," IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 4, pp. 1400-1413, 2020

    2020

  17. [24]

    A Harmonized Approach: Beyond -the-Limit Control for Autonomous Vehicles Balancing Performance and Safety in Unpredictable Environments

    S. Zhao, J. Zhang, X. He, C. He, X. Hou and H. Huang, "A Harmonized Approach: Beyond -the-Limit Control for Autonomous Vehicles Balancing Performance and Safety in Unpredictable Environments" IEEE Trans. Intell. Transp. Syst., vol. 25, no. 11, pp. 15827-15840, 2024

    2024

  18. [25]

    Trajectory and Velocity Planning Method of Emergency Rescue Vehicle Based on Segmented Three - Dimensional Quartic Bezier Curve,

    T. Chen, Y. Cai, L. Chen, X. Xu, "Trajectory and Velocity Planning Method of Emergency Rescue Vehicle Based on Segmented Three - Dimensional Quartic Bezier Curve," IEEE Trans. Intell. Transp. Syst., vol. 24, no. 3, pp 3461-3475, 2022

    2022

  19. [26]

    Deep reinforcement learning aided variable -frequency triple-phase-shift control for dual -active-bridge converter,

    Y. Tang et al., "Deep reinforcement learning aided variable -frequency triple-phase-shift control for dual -active-bridge converter," IEEE Trans. Ind. Electron., vol. 70, no. 10, pp. 10506-10515, 2022

    2022

  20. [27]

    Twin -delayed ddpg: A deep reinforcement learning technique to model a continuous movement of an intelligent robot agent,

    S. Dankwa and W. Zheng, "Twin -delayed ddpg: A deep reinforcement learning technique to model a continuous movement of an intelligent robot agent," in Proc. 3rd Int. Conf. Vis. Image Signal Process., 2019, pp. 1-5

    2019

  21. [28]

    A feedback-feed- forward steering control strategy for improving lateral dynamics stability of an A-double vehicle at high speeds,

    M. S. Kati, J. Fredriksson, B. Jacobson, and L. Laine, "A feedback-feed- forward steering control strategy for improving lateral dynamics stability of an A-double vehicle at high speeds," Veh. Syst. Dyn., vol. 60, no. 11, pp. 3955-3976, 2022

    2022

  22. [29]

    A Three -Dimensional Dynamics Control Framework of Vehicle Lateral Stability and Rollover Prevention via Active Braking With MPC,

    L. Li, Y. Liu, R. Wang, J. Chen, " A Three -Dimensional Dynamics Control Framework of Vehicle Lateral Stability and Rollover Prevention via Active Braking With MPC," IEEE Trans. Ind. Electron., vol. 64, no. 4, pp. 3389-3401, 2017

    2017

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