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首页> 外文会议>Unmanned Systems Technology IX; Proceedings of SPIE-The International Society for Optical Engineering; vol.6561 >Detection, Tracking, and Avoidance of Moving Objects from a Moving Autonomous Vehicle
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Detection, Tracking, and Avoidance of Moving Objects from a Moving Autonomous Vehicle

机译：从自动驾驶汽车中检测，跟踪和避免移动物体

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ARL is developing the autonomous capability to directly support the Army's future requirements to employ unmanned systems. The purpose of this paper is to document and benchmark the current ARL Collaborative Technology Alliance (CTA) capabilities in detecting, tracking and avoiding moving humans and vehicles from a moving unmanned vehicle. For this experiment ARL and General Dynamics Robotic Systems (GDRS) conducted an experiment involving an ARL experimental Unmanned Vehicle (XUV) operating in proximity to a number of stationary and moving human surrogates (mannequins) and moving vehicles. In addition there were other objects along the XUV route of the experiment such as barrels, fire hydrants, poles, cones, and other clutter. The experiment examined the performance of seven algorithms using a series of sensor modalities to detect stationary and moving objects. Three of the algorithms showed promise, detecting human surrogates and vehicles with probabilities ranging from 0.64 to 0.85, while limiting probability of misclassification to 0.14 to 0.37. Moving mannequins were detected with slightly higher probabilities than fixed mannequins. The distance from the ground truth at the time of detection suggests that at a speed of 20 kph with a minimum distance to detection of 19.38 m, the vehicle would have a minimum of 3.5 seconds to avoid a mannequin or vehicle if detected by one of these three algorithms. Among mannequins and vehicles and, mannequins were more frequently detected than vehicles.
机译：ARL正在发展自主能力，以直接支持陆军使用无人系统的未来需求。本文的目的是记录和基准化当前的ARL协作技术联盟（CTA）在检测，跟踪和避免移动人员和无人驾驶车辆中移动人员和车辆的能力。对于该实验，ARL和通用动力机器人系统（GDRS）进行了一项涉及ARL实验无人飞行器（XUV）的实验，该无人飞行器在许多固定和移动的人类替代物（甘露聚糖）和移动的车辆附近运行。另外，实验的XUV路线上还有其他物体，例如桶，消火栓，电线杆，圆锥和其他杂物。该实验使用一系列传感器模式检测静止和运动物体，检验了七种算法的性能。其中三种算法表现出了希望，可检测概率介于0.64至0.85之间的人类替代物和车辆，同时将错误分类的可能性限制在0.14至0.37之间。检测到移动人体模型的概率比固定人体模型高一些。与检测时与地面真相的距离表明，以20 km / h的速度，最短检测距离为19.38 m，如果被其中之一检测到，则该车辆将至少有3.5秒的时间来避开人体模型或车辆三种算法。在人体模型和车辆中，人体模型比车辆更常见。

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