Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving.
Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon
