A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Rasekhipour, Yadollah; Khajepour, Amir; Chen, Shih-Ken; Litkouhi, Bakhtiar

doi:10.1109/tits.2016.2604240

Cited by 509 publications

(216 citation statements)

References 28 publications

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“…Optimal control approaches usually map the optimal control problem to a nonlinear programming (NLP) problem that can be solved using numerical NLP solvers, see for example [30,31,28]. Although, potential field and optimal control methods are quite popular due to the intuitive problem formulation [32], there are still open issues regarding the decision making process.…”

Section: Related Workmentioning

confidence: 99%

Deep reinforcement‐learning‐based driving policy for autonomous road vehicles

Makantasis

Kontorinaki

Nikolos

2019

IET Intelligent Transport Systems

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In this work we consider the problem of path planning for an autonomous vehicle that moves on a freeway. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, we propose the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. We consider driving scenarios where the road is occupied both by autonomous and manual driving vehicles. To the best of our knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, we present some initial results regarding the effect of autonomous vehicles' behavior on the overall traffic flow.

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Section: Related Workmentioning

confidence: 99%

Deep reinforcement‐learning‐based driving policy for autonomous road vehicles

Makantasis

Kontorinaki

Nikolos

2019

IET Intelligent Transport Systems

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“…GPS and INS are used to calculate the driving position, vehicle speed, and other information of the unmanned driving vehicle. The navigation parameters are analyzed to guide the unmanned driving IWM-EV to operate along the selected route accurately and safely [20][21][22][23].…”

Section: Complexitymentioning

confidence: 99%

GA‐BPNN Based Hybrid Steering Control Approach for Unmanned Driving Electric Vehicle with In‐Wheel Motors

Wang

2018

Complexity

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The steering system is a key component of the unmanned driving electric vehicle with in-wheel motors (IWM-EV), which is closely related to the operating safety of the vehicle. To characterize the complex nonlinear structure of the steering system of unmanned driving IWM-EV, a hierarchical modeling and hybrid steering control approach are presented. Firstly the 2-DOF model is introduced for the entire vehicle system, and then the models of the steering system and the in-wheel drive system are analyzed sequentially. The steering torque control system based on electronic differential (ED) and differential assist steering (DAS) is studied. The back propagation neural network (BPNN) is used to optimize the network structure, parameters, and the weight coefficient of the hybrid steering system. The genetic algorithm (GA) is employed to optimize the initial weight of BPNN and search within a large range. The GA-BPNN model is established with the yaw moment and differential torque as the input of BPNN. Simulation and experimental results show that the proposed GA-BPNN-based hybrid steering control approach not only accelerates the convergence speed of steering torque weight adjustment but also improves the response speed and flexibility of the steering system. Through optimizing and distributing the steering torque dynamically, the proposed GA-BPNN-based control approach has inherited the advantages of both vehicle stability under ED and the steering assistance under DAS, which further guarantees the safety and stability of unmanned driving IWM-EV.

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“…According to statistical data, most traffic accidents are caused by drivers' operational errors. 1 Autonomous vehicles don't need drivers' intervention, so it can reduce the traffic accidents caused by drivers' misoperation. In recent years, autonomous vehicles have become a hotspot in the field of vehicle engineering and a new driving force for the growth of the automotive industry.…”

Section: Introductionmentioning

confidence: 99%

Path tracking of autonomous vehicle based on adaptive model predictive control

Lin

Chen

Zhao

et al. 2019

International Journal of Advanced Robotic Systems

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In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.

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A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Cited by 509 publications

References 28 publications

Deep reinforcement‐learning‐based driving policy for autonomous road vehicles

Deep reinforcement‐learning‐based driving policy for autonomous road vehicles

GA‐BPNN Based Hybrid Steering Control Approach for Unmanned Driving Electric Vehicle with In‐Wheel Motors

Path tracking of autonomous vehicle based on adaptive model predictive control

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