Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling.

For autonomous vehicles, it is an important requirement to obtain integrate static road information in real-time in dynamic driving environment. A comprehensive perception of the surrounding road should cover the accurate detection of the entire road area despite occlusion, the 3D geometry and the types of road topology in order to facilitate the practical applications in autonomous driving. To this end, we propose a lightweight and efficient LiDAR-based multi-task road perception network (LMRoadNet) to conduct occlusion-free road segmentation, road ground height estimation, and road topology recognition simultaneously. To optimize the proposed network, a corresponding multi-task dataset, named MultiRoad, is built semi-automatically based on the public SemanticKITTI dataset. Specifically, our network architecture uses road segmentation as the main task, and the remaining two tasks are directly decoded on a concentrated 1 / 4 scale feature map derived from the main task's feature maps of different scales and phases, which significantly reduces the complexity of the overall network while achieves high performance. In addition, a loss function with learnable weight of each task is adopted to train the neural network, which effectively balances the loss of each task and improves performance of the individual tasks. Extensive experiments on the test set show that the proposed network achieves great performance of the three tasks in real-time, outperforms the conventional multi-task architecture and is comparable to the state-of-the-art efficient methods. Finally, a fusion strategy is proposed to combine results on different directions to expand the field of view for practical applications.