Vehicle trajectory prediction is a crucial but intricate problem for lateral driving assistance systems because of driver uncertainty. This article presents a probabilistic vehicle-trajectory prediction method based on a dynamic Bayesian network (DBN) model integrating the driver’s intention, maneuvering behavior, and vehicle dynamics. By selecting a most-relevant-feature vector using joint mutual information, we design a Gaussian mixture model- hidden Markov model and employ the model as a node in the DBN to identify the driver’s intention. Then, a reference path is generated using the road information. The uncertainties of drivers are captured in steering- and longitudinal-control using a stochastic driver model and a Markov chain, respectively. A vehicle dynamic model ensures that the predicted vehicle trajectory adheres to the vehicle dynamics, which improves the prediction accuracy. A particle filter is used to recursively estimate the vehicle trajectory, including position coordinates and the lateral distance from the vehicle center of gravity to the road edge. We evaluate the proposed DBN trajectory prediction method in both lane-keeping and lane-changing scenarios based on a dataset collected from a real-time dynamic driving simulator. Results show that the proposed method can achieve accurate long-term trajectory prediction.

Autonomous racing has attracted extensive interest due to its great potential in self-driving at the extreme limits. Model-based and learning-based methods are widely used in autonomous racing. Out of which, model-based methods cannot cope with complex environments when only local perception is available. This limit can be overcome by the Proximal policy optimization (PPO), a typical learning-based method, which does not excessively rely on global perception. However, existing PPO faces challenges with low training efficiency in long sequences. To solve this issue, this paper develops an improved PPO by introducing a curiosity mechanism, a balanced reward function, and an image efficient actor-critic network. The curiosity mechanism focuses on training on key segments, facilitating efficient short-term learning of the PPO. The balanced reward function adjusts rewards based on the complexity of racetracks, promoting efficient exploration of the control strategy during training. The image-efficient actorcritic network enhances the PPO to fast process the perceived information. Simulation results on a physical engine demonstrate that the proposed algorithm outperforms benchmark algorithms in achieving less number of collisions, higher peak reward with less training time, and shorter laptime among multiple testing racetracks