Risk-based autonomous vehicle motion control with considering human driver’s behaviour

Abstract The selected motions of autonomous vehicles (AVs) are subject to the constraints from the surrounding traffic environment, infrastructure and the vehicle’s dynamic capabilities. Normally, the motion control of the vehicle is composed of trajectory planning and trajectory following according to the surrounding risk factors, the vehicles’ capabilities as well as tyre/road interaction situations. However, pure trajectory following with a unique path may make the motion control of the vehicle be too careful and cautious with a large amount of steering effort. To follow a planned trajectory, the AVs with the traditional path-following control algorithms will correct their states even if the vehicles have only a slight deviation from the desired path or the vehicle detects static infrastructure like roadside trees. In this case, the safety of the AVs can be guaranteed to some degree, but the comfort and sense of hazards for the drivers are ignored, and sometimes the AVs have unusual motion behaviours which may not be acceptable to other road users. To solve this problem, this study aims to develop a safety corridor-based vehicle motion control approach by investigating human-driven vehicle behaviour and the vehicle’s dynamic capabilities. The safety corridor is derived by the manoeuvring action feedback of actual drivers as collected in a driving simulator when presented with surrounding risk elements and enables the AVs to have safe trajectories within it. A corridor-based Nonlinear Model Predictive Control (NMPC) has been developed which controls the vehicle state to achieve a smooth and comfortable trajectory while applying trajectory constraints using the safety corridor. The safety corridor and motion controller are assessed using four typical scenarios to show that the vehicle has a human-like or human-oriented behaviour which is expected to be more acceptable for both drivers and other road users.

[1]  Rongrong Wang,et al.  Robust H∞ output-feedback control for path following of autonomous ground vehicles , 2016 .

[2]  Duanfeng Chu,et al.  A method of vehicle motion prediction and collision risk assessment with a simulated vehicular cyber physical system , 2014 .

[3]  Maxim Likhachev,et al.  Motion planning in urban environments: Part I , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Meixin Zhu,et al.  Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning , 2018, Transportation Research Part C: Emerging Technologies.

[5]  Luigi Fortuna,et al.  Path planning with obstacle avoidance based on visibility binary tree algorithm , 2013, Robotics Auton. Syst..

[6]  Jin-Woo Lee,et al.  Human-like planning of swerve maneuvers for autonomous vehicles , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[7]  Chongfeng Wei,et al.  Transient dynamic behaviour of finite element tire traversing obstacles with different heights , 2014 .

[8]  Xiaoguang Yang,et al.  A finite-element-based approach to characterising FTire model for extended range of operation conditions , 2017 .

[9]  Bin Jia,et al.  A data-driven lane-changing model based on deep learning , 2019, Transportation Research Part C: Emerging Technologies.

[10]  Håkan Jonsson,et al.  Planning Smooth and Obstacle-Avoiding B-Spline Paths for Autonomous Mining Vehicles , 2010, IEEE Transactions on Automation Science and Engineering.

[11]  Fazel Naghdy,et al.  Velocity-dependent robust control for improving vehicle lateral dynamics , 2011 .

[12]  Mohammad Behroozi,et al.  Simulation of tyre rolling resistance generated on uneven road , 2016 .

[13]  Markos Papageorgiou,et al.  Optimal vehicle trajectory planning in the context of cooperative merging on highways , 2016 .

[14]  Yanjun Huang,et al.  Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints , 2017, IEEE Transactions on Vehicular Technology.

[15]  David González,et al.  A Review of Motion Planning Techniques for Automated Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[16]  Rahul Kala,et al.  Multi-Level Planning for Semi-autonomous Vehicles in Traffic Scenarios Based on Separation Maximization , 2013, J. Intell. Robotic Syst..

[17]  Julius Ziegler,et al.  Trajectory planning for Bertha — A local, continuous method , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[18]  Erwin R. Boer Satisficing Curve Negotiation: Explaining Drivers’ Situated Lateral Position Variability , 2016 .

[19]  Emilio Frazzoli,et al.  Optimal motion planning with the half-car dynamical model for autonomous high-speed driving , 2013, 2013 American Control Conference.

[20]  Jonathan P. How,et al.  Real-Time Motion Planning With Applications to Autonomous Urban Driving , 2009, IEEE Transactions on Control Systems Technology.

[21]  Hongbin Zha,et al.  A real-time motion planner with trajectory optimization for autonomous vehicles , 2012, 2012 IEEE International Conference on Robotics and Automation.

[22]  Wan-Suk Yoo,et al.  Dynamic vehicle model for handling performance using experimental data , 2015 .

[23]  David González,et al.  Continuous curvature planning with obstacle avoidance capabilities in urban scenarios , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[24]  Emilio Frazzoli,et al.  Anytime Motion Planning using the RRT* , 2011, 2011 IEEE International Conference on Robotics and Automation.

[25]  Rongrong Wang,et al.  Integral Sliding Mode-Based Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Vehicles , 2016, IEEE Transactions on Transportation Electrification.

[26]  Oluremi Olatunbosun,et al.  The effects of tyre material and structure properties on relaxation length using finite element method , 2016 .

[27]  Gianluca Antonelli,et al.  A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking , 2007, IEEE Transactions on Fuzzy Systems.

[28]  Myoungho Sunwoo,et al.  Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles , 2012, IEEE Transactions on Intelligent Transportation Systems.