Deep reinforcement learning-based drift parking control of automated vehicles

Leng, Bo; Yu, Yang; Liu, Ming; Cao, Lei; Yang, Xing; Liu, Xiong

doi:10.1007/s11431-022-2273-5

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Firstly, the incremental linear equation of state is iterated to obtain the prediction equation ： 𝒀 = 𝝍𝝃 ̂(𝑘) + 𝜣Δ𝑼 (10) The design cost function is:…”

Section: Mpc Steady Drift Controllermentioning

confidence: 99%

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Research on steady state drift control of unmanned vehicle based on model predictive control

Feng,

Jing,

Kuang

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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Traditional unmanned vehicles are generally limited to work under ordinary conditions, however the control strategy under extreme conditions still needs to be studied. This paper proposes a drift controller based on Model Predictive Control (MPC). Firstly, the saturation characteristics of the tire in the drift state are analyzed. On this basis, the drift steering mechanism of the vehicle model is analyzed, and the drift equilibrium point is obtained. Then the vehicle dynamics model is linearized at the equilibrium point, and the MPC drift controller is designed to realize the steady-state drift control of the vehicle. Finally, the effectiveness of the steady-state drift controller is verified by simulation.

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“…Firstly, the incremental linear equation of state is iterated to obtain the prediction equation ： 𝒀 = 𝝍𝝃 ̂(𝑘) + 𝜣Δ𝑼 (10) The design cost function is:…”

Section: Mpc Steady Drift Controllermentioning

confidence: 99%

“…Cai et al [9] designed drift controller based on AC deep reinforcement learning algorithm. Leng et al [10] introduced TD3 reinforcement learning algorithm into drift control to achieve accurate drift parking function.…”

Section: Introductionmentioning

confidence: 99%

Research on steady state drift control of unmanned vehicle based on model predictive control

Feng,

Jing,

Kuang

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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show abstract

“…Over the past decade, extensive research has been conducted on trajectory control for CAVs. A variety of control strategies have been developed, including adaptive cruise control (ACC) [8], cooperative adaptive cruise control (CACC) [9,10], model predictive control (MPC) [11][12][13], and deep reinforcement learning (DRL) control [14][15][16][17], are developed to optimize the trajectories of CAVs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

Li,

Dong,

et al. 2024

Journal of Advanced Transportation

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One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.

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“…[37] successfully developed an agent that can even drift on arbitrary trajectories in simulation. Also, as with control methods, drift parking has been attempted with RL too [38]. In addition to these works, an autonomous racing task, including control at handling limits, was also attempted with a model-based policy search [39].…”

Section: Introductionmentioning

confidence: 99%

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

Tóth,

Viharos,

Bárdos

et al. 2024

Vehicles

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Enhancing the safety of passengers by venturing beyond the limits of a human driver is one of the main ideas behind autonomous vehicles. While drifting is mostly witnessed in motorsports as an advanced driving technique, it could provide many possibilities for improving traffic safety by avoiding accidents in extreme traffic situations. The purpose of the research presented in this article is to provide a machine learning-based solution to autonomous drifting as a proof of concept for vehicle control at the limits of handling. To achieve this, reinforcement learning (RL) agents were trained for the task in a MATLAB/Simulink-based simulation environment, using the state-of-the-art Soft Actor–Critic (SAC) algorithm. The trained agents were tested in reality at the ZalaZONE proving ground on a series production sports car with zero-shot transfer. Based on the test results, the simulation environment was improved through domain randomization, until the agent could perform the task both in simulation and in reality on a real test car.

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Deep reinforcement learning-based drift parking control of automated vehicles

Cited by 4 publications

References 17 publications

Research on steady state drift control of unmanned vehicle based on model predictive control

Research on steady state drift control of unmanned vehicle based on model predictive control

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

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

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