Predicting Driver Intent and Maneuvers for Road Safety

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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).