Predicting Driver Intent and Maneuvers for Road Safety

该研究计划的重点在于识别影响交通安全的驾驶认知因素，定义自动驾驶汽车安全技术设计的合理原则，以及开发智能高级驾驶辅助系统（i-ADAS），并将驾驶员行为预测和纠正作为安全改进的核心原则。更确切地说，本研究的目的是开发驾驶机动的操作理解，可用于预测引擎的概念纳入未来的实现i-ADAS。为此，我们仪器化的实验车辆能够记录其直接正面环境中的3D，车辆里程计，驾驶员操纵和车辆功能的操作，驾驶员头眼行为（头部姿势和3D凝视），和3D点凝视（PoG）在绝对坐标内感知的3D正面环境的车辆。我们在安大略省伦敦市周围的预定义路径上从16名驾驶员那里获取了超过3 TB的数据。设计了自动注释该数据集的算法。例如，在标记过程中采用了多车道检测、车辆检测、地平面检测和GPS校正技术的新技术。我们现在正在设计的技术，语义段的三维立体数据流使用时空相干性相关的约束，以获得对象描述符沿着与他们的三维包围体。反过来，驾驶员的3D凝视可以用这些包围体来表示，从而允许识别驾驶员正在看什么以及在哪里。我们的兴趣是在0.25秒至约2秒的时间窗口内预测最可能的驾驶员操纵：当预测的操纵与车辆和驾驶员发现自己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，并避免较大时间窗口带来的预测可靠性问题。最近使用我们的数据和使用逻辑回归的3层神经网络进行的实验表明，对于代表大约一个小时驾驶的数据序列，可以预测接下来0.5到1秒的规范机动加速和减速，准确率为99.6%。其他规范机动已经预测到了这种准确度。我们目前正在开发和测试并发的方法来解决驾驶员操纵预测的问题。我们非常有兴趣确定驾驶序列中的哪些数据流对机动预测影响最大（有证据表明头眼行为在这方面很重要）。

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).