Pylot：用于探索自动驾驶车辆延迟-准确性权衡的模块化平台
Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles
摘要：
我们介绍了Pylot，一个用于自动驾驶汽车（AV）研究和开发的平台，其目标是让研究人员研究其模型和算法的延迟和准确性对AV端到端驾驶行为的影响。这是通过模块化结构实现的，该模块化结构由我们的高性能Analog系统实现，该系统代表AV软件管道组件（对象检测器，运动规划器等）。作为使用时间戳消息在数据流上通信的操作符的图。Pylot易于与流行的AV模拟器（如CARLA）接口，并且可以轻松部署到真实世界的车辆中，只需最少的代码更改。为了减少开发整个管道来评估单个组件的负担，Pylot为AV管道的各种组件提供了几个最先进的参考实现。使用这些参考实现，基于Pylot的AV管道能够驾驶真实车辆，并在CARLA自动驾驶挑战赛中获得高分。我们还提出了几个案例研究，使Pylot，包括需要上下文相关的组件，和每个组件的时间分配的证据。Pylot是开源的，代码可以在https://github.com/erdos-project/pylot上找到。
英文摘要：
We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-end driving behavior of an AV. This is achieved through a modular structure enabled by our high-performance dataflow system that represents AV software pipeline components (object detectors, motion planners, etc.) as a dataflow graph of operators which communicate on data streams using timestamped messages. Pylot readily interfaces with popular AV simulators like CARLA, and is easily deployable to real-world vehicles with minimal code changes. To reduce the burden of developing an entire pipeline for evaluating a single component, Pylot provides several state-of-the-art reference implementations for the various components of an AV pipeline. Using these reference implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and attains a high score on the CARLA Autonomous Driving Challenge. We also present several case studies enabled by Pylot, including evidence of a need for context-dependent components, and per-component time allocation. Pylot is open source, with the code available at https://github.com/erdos-project/pylot.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
