Lateral Control of Heavy Vehicles

Sammanfattning: The automotive industry has been involved in making vehicles autonomous to different levels in the past decade rapidly. Particularly in the commercial vehicle market, there is a significant necessity to make trucks have a certain level of automation to help reduce dependence on human efforts to drive. This could help in reducing several accidents caused by human error. Interestingly there are several challenges and solutions in achieving and implementing autonomous driving for trucks. First, a benchmark of different control architectures that can make a truck drive autonomously are explored. The chosen controllers (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control and Model Predictive Control) vary in their simplicity in implementation and versatility in handling different vehicle parameters and constraints. A thorough comparison of these path tracking controllers are performed using several metrics. Second, a collision avoidance system based on cubic polynomials, inspired by rapidly exploring random tree (RRT) is presented. Some of the path tracking controllers are limited by their ability and hence a standalone collision avoidance system is needed to provide safe maneuvering. Simulations are performed for different test cases with and without obstacles. These simulations help compare safety margin and driving comfort of each path tracking controller that are integrated with the collision avoidance system. Third, different performance metrics like change in acceleration input, change in steering input, error in path tracking, deviation from base frame of track file and lateral and longitudinal margin between ego and target vehicle are presented. To conclude, a set of suitable controllers for heavy articulated vehicles are developed and benchmarked.