Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

Abstract

Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

Intelligent Transportation Related Complex Systems and Sensors

“…The development of human-like autonomous driving systems has in recent times gained increased attention from both technology companies and academic institutions alike, as it has the potential to improve the interpretability and acceptance of autonomous vehicle systems. [ 9 ] addressed the research gap found in the planning of a safe and human-like obstacle avoidance trajectory, which is one of the critical issues for the development of autonomous vehicles (AVs). The paper proposed an automatic obstacle avoidance system that focused on the obstacle avoidance characteristics of human drivers.…”

Intelligent Transportation Related Complex Systems and Sensors

“…The development of human-like autonomous driving systems has in recent times gained increased attention from both technology companies and academic institutions alike, as it has the potential to improve the interpretability and acceptance of autonomous vehicle systems. [9] addressed the research gap found in the planning of a safe and humanlike obstacle avoidance trajectory, which is one of the critical issues for the development of autonomous vehicles (AVs). The paper proposed an automatic obstacle avoidance system that focused on the obstacle avoidance characteristics of human drivers.…”

An Optimized Trajectory Planner and Motion Controller Framework for Autonomous Driving in Unstructured Environments

“…The optimal control problem for each sampling time is converted into a quadratic programming problem, while the MPC controller can effectively reduce the tracking error and ensure the stability of the vehicle. Sun [ 37 ] proposed a human-like trajectory planning and tracking model based on a model predictive control algorithm, and which considers the drivers’ operational characteristics. Liu J [ 38 ] considered the tracking effect and steering stability simultaneously, based on a three-degree-of-freedom nonlinear vehicle model.…”