In this paper, we propose a novel Risk-Aware Local Trajectory Planner (RALTPER) for autonomous vehicles in complex environments characterized by Gaussian uncertainty. The proposed method integrates risk awareness and trajectory planning by leveraging probabilistic models to evaluate the likelihood of collisions with dynamic and static obstacles. The RALTPER focuses on collision avoidance constraints for both the ego vehicle region and the Gaussian-obstacle risk region. Additionally, this work enhances the generalization of both vehicle and obstacle models, making the planner adaptable to a wider range of scenarios. Our approach formulates the planning problem as a nonlinear optimization, solved using the IPOPT solver within the CasADi environment. The planner is evaluated through simulations of various challenging scenarios, including complex, static, mixed environment and narrow single-lane avoidance of pedestrians. Results demonstrate that RALTPER achieves safer and more efficient trajectory planning particularly in navigating narrow areas where a more accurate vehicle profile representation is critical for avoiding collisions.

翻译：本文提出了一种新颖的风险感知局部轨迹规划器（RALTPER），用于高斯不确定性表征的复杂环境中的自动驾驶车辆。该方法通过利用概率模型评估与动态及静态障碍物发生碰撞的可能性，将风险感知与轨迹规划相结合。RALTPER重点关注自车区域和高斯障碍物风险区域的碰撞规避约束。此外，本研究增强了对车辆和障碍物模型的泛化能力，使得规划器能够适应更广泛的场景。我们的方法将规划问题表述为一个非线性优化问题，并在CasADi环境中使用IPOPT求解器进行求解。通过对多种具有挑战性的场景（包括复杂的静态环境、混合环境以及狭窄单车道行人避让）进行仿真，对该规划器进行了评估。结果表明，RALTPER实现了更安全、更高效的轨迹规划，特别是在导航狭窄区域时，更精确的车辆轮廓表示对于避免碰撞至关重要。