Pioneers of autonomous vehicles (AVs) promised to revolutionize the driving experience and driving safety. However, milestones in AVs have materialized slower than forecast. Two culprits are (1) the lack of verifiability of proposed state-of-the-art AV components, and (2) stagnation of pursuing next-level evaluations, e.g., vehicle-to-infrastructure (V2I) and multi-agent collaboration. In part, progress has been hampered by: the large volume of software in AVs, the multiple disparate conventions, the difficulty of testing across datasets and simulators, and the inflexibility of state-of-the-art AV components. To address these challenges, we present AVstack, an open-source, reconfigurable software platform for AV design, implementation, test, and analysis. AVstack solves the validation problem by enabling first-of-a-kind trade studies on datasets and physics-based simulators. AVstack solves the stagnation problem as a reconfigurable AV platform built on dozens of open-source AV components in a high-level programming language. We demonstrate the power of AVstack through longitudinal testing across multiple benchmark datasets and V2I-collaboration case studies that explore trade-offs of designing multi-sensor, multi-agent algorithms.

翻译：自动驾驶车辆（AV）的先驱曾承诺将彻底改变驾驶体验和驾驶安全性。然而，AV的里程碑进展比预期缓慢。两个主要原因是：（1）提出的最先进AV组件缺乏可验证性；（2）追求下一代评估（如车路协同和多智能体协作）的停滞。部分原因在于：AV中庞大的软件体量、多种不兼容的惯例、跨数据集和模拟器测试的困难，以及最先进AV组件的僵化性。为解决这些挑战，我们提出AVstack——一个用于AV设计、实现、测试和分析的开源、可重构软件平台。AVstack通过首次支持基于数据集和物理模拟器的权衡研究，解决了验证问题。AVstack作为一个基于数十种开源AV组件、采用高级编程语言构建的可重构平台，解决了停滞问题。我们通过跨多个基准数据集的纵向测试以及探索多传感器、多智能体算法设计权衡的V2I协作案例研究，展示了AVstack的强大功能。