自动驾驶决策规划算法的鲁棒性挑战与前沿对策

Robustness Challenges and Cutting-Edge Countermeasures for Autonomous Driving Decision-Making and Planning Algorithms

摘要: 随着传感器与感知模型不断迭代，自动驾驶感知能力已有明显提升，但道路长尾突发场景、人车交互不确定性仍是高阶自动驾驶落地瓶颈。本文提出，自动驾驶当前主要矛盾已由感知硬件短板，转变为决策规划模块认知鲁棒性不足，而复杂的验证测试体系会加剧决策失效风险。文章结合典型事故与行业测试数据，从分布外场景泛化、人车混行交互、感知噪声下决策容错、系统验证难题四方面剖析内在挑战；对比强化学习、世界模型、神经符号融合、车路协同辅助决策等前沿解决路径；最终从算法架构、测试验证、制度配套维度提出改进策略，为增强自动驾驶决策系统的实际可靠性提供理论支撑。

Abstract: With the continuous iteration of sensors and perception models, the perception capability of autonomous driving has been significantly improved. However, long-tail emergency scenarios on roads and uncertainties in human-vehicle interactions remain bottlenecks for the deployment of high-level autonomous driving. This paper argues that the primary contradiction in autonomous driving has shifted from shortcomings in perception hardware to insufficient cognitive robustness of the decision-making and planning module, and the complex verification and testing system exacerbates the risk of decision failures. Combining typical accident cases and industrial test data, the paper analyzes inherent challenges from four perspectives: generalization to out-of-distribution scenarios, interactions in mixed human-vehicle traffic, decision fault tolerance under perceptual noise, and difficulties in system verification. It compares cutting-edge solutions including reinforcement learning, world models, neuro-symbolic fusion, and vehicle-road collaborative assisted decision-making. Finally, improvement strategies are proposed from the dimensions of algorithm architecture, test verification and institutional support, providing theoretical support for enhancing the practical reliability of autonomous driving decision systems.