Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. Thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are. Similarly, few platforms exist for much-needed multi-agent analysis. To provide a starting point for analysis of sensor fusion and collaborative & distributed sensing, we design an accessible, modular sensing platform with AVstack. We build collaborative and distributed camera-radar fusion algorithms and demonstrate an evaluation ecosystem of AV datasets, physics-based simulators, and hardware in the physical world. This three-part ecosystem enables testing next-generation configurations that are prohibitively challenging in existing development platforms.

翻译：主流的自动驾驶汽车平台和测试基础设施，遗憾的是，均为专有且闭源。因此，评估安全关键型自动驾驶汽车的性能及其真实安全水平十分困难。同样，对于迫切需要的多智能体分析，目前也鲜有现成平台。为了给传感器融合及协作与分布式感知分析提供起点，我们基于AVstack设计了一个可访问的模块化感知平台。我们构建了协作式分布式摄像头-雷达融合算法，并展示了一个包含自动驾驶数据集、基于物理的仿真器以及真实世界硬件的评估生态系统。这一三部分生态系统能够测试在现有开发平台中难以实现的下一代配置方案。