Cyclops, introduced in this paper, is an open research platform for everyone that wants to validate novel ideas and approaches in the area of self-driving heavy-duty vehicle platooning. The platform consists of multiple 1/14 scale semi-trailer trucks, a scale proving ground, and associated computing, communication and control modules that enable self-driving on the proving ground. A perception system for each vehicle is composed of a lidar-based object tracking system and a lane detection/control system. The former is to maintain the gap to the leading vehicle and the latter is to maintain the vehicle within the lane by steering control. The lane detection system is optimized for truck platooning where the field of view of the front-facing camera is severely limited due to a small gap to the leading vehicle. This platform is particularly amenable to validate mitigation strategies for safety-critical situations. Indeed, a simplex structure is adopted in the embedded module for testing various fail safe operations. We illustrate a scenario where camera sensor fails in the perception system but the vehicle operates at a reduced capacity to a graceful stop. Details of the Cyclops including 3D CAD designs and algorithm source codes are released for those who want to build similar testbeds.
翻译:本文介绍的独眼巨人是一个开放的研究平台,供所有希望验证自行驾驶重型车辆排队领域新颖想法和新做法的人使用,平台由多个1/14级半拖车级半拖车、一个规模校准地面和相关计算、通信和控制模块组成,能够在证明地自行驾驶。每辆车的感知系统由一个基于Lidar的物体跟踪系统和一个航道探测/控制系统组成。前者是保持对主要车辆的缺口,后者是通过方向控制在车道内维护车辆。对于卡车排队而言,车道探测系统是优化的,因为前台摄影机的视野领域由于领先车辆的很小的缺口而受到严重限制。这个平台特别适合验证安全危急情况下的缓解战略。事实上,在嵌入式模块中采用了一个简单的结构,用于测试各种故障安全操作。我们举例说明了一种情景,即摄像器传感器在感系统中失灵,但车辆的操作能力却被降低到优缓停状态。包括3D CAD设计和算法源码在内的车道详细信息,需要为类似的测试室码。