Abstract
This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake pedals, and an Nvidia drive PX2 with a robot operating system (ROS). The vehicle dynamics model, sensors, controller, and test field map are virtually built with the PreScan simulation platform. Both manual and autonomous driving modes can be simulated, and a graphic user interface allows a test driver to select a target parking space on a display screen. Three scenarios are demonstrated: forward parking, reverse parking, and obstacle avoidance. When the vehicle perceives an obstacle, the map is updated and the route is adaptively planned. The effectiveness of the proposed MPC is verified in experiments and proved to be superior to a traditional proportional–integral–derivative controller with regards to safety, energy-saving, comfort, and agility.
Highlights
Forward parking, reverse parking, and obstacle avoidance
Various advanced driver assistance system (ADAS) modules have been put on the market, such as adaptive cruise control (ACC), autonomous emergency braking system (AEB), lane keeping system (LKS), etc
The path planning must convey to the vehicle the knowledge of its surrounding environment so that the vehicle control unit can command its actuators with appropriate actions in real time
Summary
The advanced development of science and technology has made many researchers put their efforts to enhance safety, energy-saving, comfort, and agility in road traffic through vehicle automation. Alternative methods of gain scheduling, parameter adaptation, and auto-tuning might be able to upgrade PID control performances [13] with or without vehicle models More advanced techniques, such as sliding mode control [14], pure pursuit control [15,16], optimal predictive control [17], iterative linear quadratic regulator (LQR) [18], nonlinear model predictive control [19], and robust control [20] have been proposed to control the vehicle with complex dynamic behaviors. This paper combines hybrid A-star path planning and time varying linear MPC with vehicle dynamic models for an electric van (e-van) to simulate self-driving and parking on a hardware-in-the-loop (HiL) simulation platform.
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