提出并行共识优化方法，实现自动驾驶在感知受限下的安全一致规划。
Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization
- 基于共识ADMM分解轨迹优化问题，分治求解提升效率。
- 在合成与真实数据集上验证，显著提升安全性和行驶一致性。
- 适合需要实时性与鲁棒性的自动驾驶系统研发者参考。
在部分可观测环境中，确保自动驾驶车辆的安全性与驾驶一致性是一项重大挑战。本文提出一种一致的并行轨迹优化（CPTO）方法，以应对高密度障碍物环境中的感知不确定性。基于离散时间屏障函数理论，设计了一种共识安全屏障模块，在时空轨迹空间中对多种可能的障碍物配置提供可靠的安全覆盖。随后，推导出一个双凸并行轨迹优化问题，可分解为一系列低维二次规划子问题，加速计算。通过采用共识交替方向乘子法（ADMM）进行并行优化，每个生成的候选轨迹对应一种环境配置，同时共享一个共识轨迹段。该共识轨迹段可在实时执行中保障车辆安全与行为一致性。我们在多个驾驶任务中，通过与先进基线方法的广泛对比，验证了CPTO框架的有效性。实验结果表明，无论在合成数据还是真实交通数据集上，该方法均实现了更高的安全性与一致性。
原文摘要 · Abstract (English)
Ensuring safety and driving consistency is a significant challenge for autonomous vehicles operating in partially observed environments. This work introduces a consistent parallel trajectory optimization (CPTO) approach to enable safe and consistent driving in dense obstacle environments with perception uncertainties. Utilizing discrete-time barrier function theory, we develop a consensus safety barrier module that ensures reliable safety coverage within the spatiotemporal trajectory space across potential obstacle configurations. Following this, a bi-convex parallel trajectory optimization problem is derived that facilitates decomposition into a series of low-dimensional quadratic programming problems to accelerate computation. By leveraging the consensus alternating direction method of multipliers (ADMM) for parallel optimization, each generated candidate trajectory corresponds to a possible environment configuration while sharing a common consensus trajectory segment. This ensures driving safety and consistency when executing the consensus trajectory segment for the ego vehicle in real time. We validate our CPTO framework through extensive comparisons with state-of-the-art baselines across multiple driving tasks in partially observable environments. Our results demonstrate improved safety and consistency using both synthetic and real-world traffic datasets.
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