BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving
BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving
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BigRoad：扩展道路数据采集以实现可靠的自动驾驶
DOI：
10.1145/3081333.3081344
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
R. Martin
中科院分区：
文献类型：
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作者：
Luyang Liu;Hongyu Li;Jian Liu;Çagdas Karatas;Yan Wang;M. Gruteser;Yingying Chen;R. Martin
先进的驾驶员辅助系统，尤其是自动驾驶，为改变道路行驶的安全性、效率和舒适性提供了前所未有的机会。开发这种安全技术不仅需要了解常见的公路和城市交通情况，还需要了解大量不同的异常事件（例如，道路上的物体和行人穿越公路等）。虽然每一次这样的事件可能都很罕见，但总的来说，它们代表了技术必须解决的重大风险，以开发真正可靠的自动驾驶和交通安全技术。通过开发将道路数据采集扩展到大量车辆的技术，本文介绍了一种低成本但可靠的解决方案BigRoad，该解决方案可以导出内部驾驶员输入（即，方向盘角度、驾驶速度和加速度）和道路环境的外部感知（即，路况和前视视频）。我们使用在3个月的时间内收集的140多个实际驾驶行程来评估收集的内部和外部数据的准确性。结果表明，BigRoad能够准确地估计方向盘转角，中位误差为0.69 °，推算车速偏差为0.65 km/h。该系统还能够通过捕获少量制动器来确定二元道路状况，准确率为95%。我们通过将收集到的视频和方向盘角度推送到深度神经网络方向盘角度预测器，进一步验证了BigRoad的可用性，显示了使用BigRoad训练自动驾驶系统的大规模数据采集的潜力。
Advanced driver assistance systems and, in particular automated driving offers an unprecedented opportunity to transform the safety, efficiency, and comfort of road travel. Developing such safety technologies requires an understanding of not just common highway and city traffic situations but also a plethora of widely different unusual events (e.g., object on the road way and pedestrian crossing highway, etc.). While each such event may be rare, in aggregate they represent a significant risk that technology must address to develop truly dependable automated driving and traffic safety technologies. By developing technology to scale road data acquisition to a large number of vehicles, this paper introduces a low-cost yet reliable solution, BigRoad, that can derive internal driver inputs (i.e., steering wheel angles, driving speed and acceleration) and external perceptions of road environments (i.e., road conditions and front-view video) using a smartphone and an IMU mounted in a vehicle. We evaluate the accuracy of collected internal and external data using over 140 real-driving trips collected in a 3-month time period. Results show that BigRoad can accurately estimate the steering wheel angle with 0.69 degree median error, and derive the vehicle speed with 0.65 km/h deviation. The system is also able to determine binary road conditions with 95% accuracy by capturing a small number of brakes. We further validate the usability of BigRoad by pushing the collected video feed and steering wheel angle to a deep neural network steering wheel angle predictor, showing the potential of massive data acquisition for training self-driving system using BigRoad.