提出自适应采样算法，快速准确估算自动驾驶中物体碰撞概率。
Fast and Accurate Collision Probability Estimation for Autonomous Vehicles using Adaptive Sigma-Point Sampling
- 基于自适应的sigma点采样，高效处理轨迹不确定性
- 中位误差3.5%，单次计算仅需0.21毫秒
- 考虑时间依赖性，避免高估碰撞风险，适合实时系统
本文提出一种新算法，用于估计具有不确定轨迹的动态物体之间的碰撞概率，其中轨迹以带有高斯分布的位姿序列形式给出。我们设计了一种自适应sigma点采样方案，最终实现一种快速、简洁的算法，在Intel Xeon Gold 6226R处理器上测试，中位误差为3.5%，中位运行时间为0.21毫秒。该算法明确考虑了碰撞概率的时间依赖性，而这一因素在以往工作中常被忽略，导致碰撞概率被高估。最后，该方法在400段6秒长的真实自动驾驶日志片段组成的多样化场景中进行了严格评估，验证了其精度与延迟表现。
原文摘要 · Abstract (English)
A novel algorithm is presented for the estimation of collision probabilities between dynamic objects with uncertain trajectories, where the trajectories are given as a sequence of poses with Gaussian distributions. We propose an adaptive sigma-point sampling scheme, which ultimately produces a fast, simple algorithm capable of estimating the collision probability with a median error of 3.5%, and a median runtime of 0.21ms, when measured on an Intel Xeon Gold 6226R Processor. Importantly, the algorithm explicitly accounts for the collision probability's temporal dependence, which is often neglected in prior work and otherwise leads to an overestimation of the collision probability. Finally, the method is tested on a diverse set of relevant real-world scenarios, consisting of 400 6-second snippets of autonomous vehicle logs, where the accuracy and latency is rigorously evaluated.
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