Due to the uncertainty of traffic participants' intentions, generating safe but not overly cautious behavior in interactive driving scenarios remains a formidable challenge for autonomous driving. In this paper, we address this issue by combining a deep learning-based trajectory prediction model with risk potential field-based motion planning. In order to comprehensively predict the possible future trajectories of other vehicles, we propose a target-region based trajectory prediction model(TRTP) which considers every region a vehicle may arrive in the future. After that, we construct a risk potential field at each future time step based on the prediction results of TRTP, and integrate risk value to the objective function of Model Predictive Contouring Control(MPCC). This enables the uncertainty of other vehicles to be taken into account during the planning process. Balancing between risk and progress along the reference path can achieve both driving safety and efficiency at the same time. We also demonstrate the security and effectiveness performance of our method in the CARLA simulator.

翻译：由于交通参与者意图的不确定性，在交互式驾驶场景中生成安全但不过度谨慎的行为仍是自动驾驶面临的一大挑战。本文通过将基于深度学习的轨迹预测模型与基于风险势场的运动规划相结合来解决这一问题。为了全面预测其他车辆可能的未来轨迹，我们提出了一种基于目标区域的轨迹预测模型（TRTP），该模型考虑车辆未来可能到达的每个区域。随后，基于TRTP的预测结果，在每个未来时间步构建风险势场，并将风险值融入模型预测轮廓控制（MPCC）的目标函数中。这使规划过程能够考虑其他车辆的不确定性。通过平衡沿参考路径的风险与前进进度，可同时实现驾驶安全性与效率。我们还在CARLA仿真器中验证了该方法的安全性与有效性。