Abstract
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.
Abstract (translated)
由于交通参与者的意图不确定性，在交互式驾驶场景中产生安全但不过于谨慎的行为仍然是对自动驾驶的一个巨大挑战。在本文中，我们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个问题。为了全面预测其他车辆可能未来的轨迹，我们提出了一个基于目标区域的轨迹预测模型（TRTP）。然后，我们根据TRTP的预测结果在每个未来时间步构建一个风险势场，并将风险价值整合到Model预测控制（MPC）的对象函数中。这使得在规划过程中可以考虑到其他车辆的不确定性。在参考路径上平衡风险和进步可以实现同时提高驾驶安全性和效率。我们还证明了我们的方法在CARLA仿真器中的安全性和有效性性能。
URL
https://arxiv.org/abs/2404.00893
