用目标漏斗提升自动驾驶路径规划在噪声感知下的稳定性
Trajectory Planning for Automated Driving using Target Funnels
- 不追踪单一轨迹，而是跟踪一系列目标集构成的漏斗
- 在噪声感知下减少56%的不当转向指令
- 适合需要高鲁棒性的自动驾驶系统开发
自动驾驶车辆依赖传感器数据实时监测周围环境，并持续适应最可能的未来道路走向。预测性路径规划依赖于对（不确定）道路走向的快照作为关键输入。在感知数据噪声较大时，道路走向估计可能显著波动，导致决策犹豫和转向行为紊乱。为解决此问题，本文提出一种新型预测路径规划算法，采用新目标函数：不以最可能的道路走向为基础追踪单一参考轨迹，而是追踪一系列称为目标漏斗的参考集合。该算法融合道路走向的概率信息，从而隐式地考虑道路感知的动态更新。通过使用原型车采集的真实驾驶数据进行案例研究，结果表明，该算法在保持跟踪精度的同时，显著减少了噪声感知下的不良转向指令，相比确定性等效方法，输入成本降低了56%。
原文摘要 · Abstract (English)
Self-driving vehicles rely on sensory input to monitor their surroundings and continuously adapt to the most likely future road course. Predictive trajectory planning is based on snapshots of the (uncertain) road course as a key input. Under noisy perception data, estimates of the road course can vary significantly, leading to indecisive and erratic steering behavior. To overcome this issue, this paper introduces a predictive trajectory planning algorithm with a novel objective function: instead of targeting a single reference trajectory based on the most likely road course, tracking a series of target reference sets, called a target funnel, is considered. The proposed planning algorithm integrates probabilistic information about the road course, and thus implicitly considers regular updates to road perception. Our solution is assessed in a case study using real driving data collected from a prototype vehicle. The results demonstrate that the algorithm maintains tracking accuracy and substantially reduces undesirable steering commands in the presence of noisy road perception, achieving a 56% reduction in input costs compared to a certainty equivalent formulation.
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