From Probabilistic Pedestrian Intent to Risk-Optimal Trajectories: A Prediction-Driven Planning Framework in Shared Spaces

With the widespread application of autonomous vehicles (AVs), their dynamic interactions with other road users pose significant challenges to trajectory planning. Previous research on trajectory planning in shared spaces has mainly focused on generating smooth trajectories, while research considering the risks of human–vehicle interactions remains insufficient. Therefore, a risk-considered trajectory planning framework for autonomous vehicles is proposed. This framework includes two modules: pedestrian trajectory prediction and vehicle planning. In the prediction module, Social-STGCNN is used to predict pedestrian trajectories, obtaining a series of trajectories and probabilities, which serve as input to the planning module. To ensure the rationality of trajectory planning, a planning model is established in Frenet coordinates based on a quintic polynomial. Combining Bayesian and equality principles, a risk-considered cost function is designed. Under this framework, the risk value is calculated using the pedestrian trajectory prediction probability, and further Bayesian and equality costs are calculated. Based on the constraints, the trajectory with the minimum cost is solved. To evaluate the rationality of this framework, we designed simulation experiments for five typical high-conflict scenarios: overtaking in the same direction, head-on collision, pedestrian crossing, encountering pedestrians from multiple directions, and turning while encountering pedestrians crossing. Simultaneously, the framework is validated in a real-world environment. The results show that the proposed method can accurately capture pedestrians’ crossing intentions and effectively avoid pedestrians. The trajectory generated in the real environment is highly consistent with that of a driver, and it exhibits excellent adaptability and robustness in high-density mixed traffic environments.