Predicting Driver Intent and Maneuvers for Road Safety

预测驾驶员意图和操作以确保道路安全

• 批准号: RGPIN-2016-04431
• 负责人: Beauchemin, Steven
• 金额: $ 1.6万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615057
• 关键词: Predicting; Driver; Intent; Maneuvers; Road

The focus of this research proposal rests on the identification of cognitive factors involved in driving that impact traffic safety, the definition of sound principles for the design of automated vehicular safety technologies, and the development of intelligent, Advanced Driving Assistance Systems (i-ADAS), with driver behaviour prediction and correction as the central tenet of safety improvement. More precisely, the objective of this research is to develop an operational understanding of driving maneuvers that may be used in the conception of prediction engines incorporated into future implementations of i-ADAS. Toward this end, we instrumented an experimental vehicle capable of recording its immediate frontal environment in 3D, the vehicle odometry, driver maneuvers and operations of vehicular functions, driver cephalo-ocular behaviour (head pose and 3D gaze), and the 3D Point of Gaze (PoG) in absolute coordinates within the perceived 3D frontal environment of the vehicle. We proceeded to obtain more than 3TB of data from 16 drivers, on a predefined path around the city of London, Ontario. Algorithms to automatically annotate this data set were devised. For instance, novel techniques for multi-lane detection, vehicle detection, ground-plane detection, and GPS-correcting techniques were employed in the labeling process. We are now devising techniques to semantically segment the 3D stereo data stream using constraints related to spatiotemporal coherence, in order to obtain object descriptors along with their 3D bounding volumes. In turn, the 3D gaze of drivers may be intersected with these bounding volumes, allowing for the identification of what the driver is looking at, in addition to where. Our interest is in predicting the most probable driver maneuver in a time window from 0.25 to about 2 seconds: this amount of time is sufficient for an i-ADAS to perform mitigating actions in cases when the predicted maneuver is inconsistent with the current traffic situation the vehicle and driver find themselves in, and avoids the prediction reliability problem posed by larger time windows. Experiments recently conducted with our data and a 3-layer neural network using logistic regression show that for a data sequence representing approximately one hour of driving the canonical maneuvers accelerate and decelerate can be predicted with an accuracy of 99.6 percent for the next 0.5 to 1 second. Other canonical maneuvers have been predicted with this level of accuracy. We are currently developing and testing concurrent approaches to the problem of driver maneuver prediction. We are very interested in determining what data streams in our driving sequences have the most impact on maneuver prediction (there is evidence that cephalo-ocular behaviour is important in this regard).

这项研究建议的重点是找出影响交通安全的驾驶认知因素，界定设计自动车辆安全技术的合理原则，以及发展智能、先进的驾驶辅助系统(I-ADAS)，并以司机行为预测和纠正为改善安全的中心宗旨。更准确地说，这项研究的目的是发展对驾驶动作的可操作性理解，可用于将预测引擎的概念纳入I-ADAS的未来实施中。为此，我们测试了一辆实验车辆，它能够以3D形式记录其即时的正面环境、车辆里程数、驾驶员的操纵和车辆功能的操作、驾驶员的头眼行为(头部姿势和3D凝视)以及在车辆感知的3D正面环境中的绝对坐标的3D凝视点(POG)。接下来，我们在安大略省伦敦市的一条预定义路径上，从16名司机那里获得了超过3TB的数据。设计了自动标注该数据集的算法。例如，在标签过程中采用了多车道检测、车辆检测、地面检测和GPS校正技术等新技术。我们现在正在设计使用与时空一致性相关的约束来对3D立体数据流进行语义分割的技术，以便获得对象描述符及其3D包围体。反过来，司机的3D凝视可以与这些边界体积相交，允许识别司机正在看的是什么，以及在哪里。我们感兴趣的是预测从0.25秒到大约2秒的时间窗口中最可能的驾驶员动作：当预测的动作与车辆和驾驶员所处的当前交通状况不一致时，这段时间足以让I-ADAS执行缓解措施，并避免更大的时间窗口带来的预测可靠性问题。最近用我们的数据和一个使用Logistic回归的三层神经网络进行的实验表明，对于一个代表大约一个小时的驾驶典型动作的数据序列，可以在接下来的0.5到1秒内以99.6%的精度预测加速和减速。其他典型的动作也已经被预测到了这个水平。我们目前正在开发和测试并发方法来解决驾驶员机动预测问题。我们非常感兴趣的是确定我们驾驶序列中的哪些数据流对机动预测的影响最大(有证据表明，在这方面头眼行为很重要)。