Title: Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network,
Abstract: Advanced driver assistance systems (ADAS) can be significantly improved with
effective driver action prediction (DAP). Predicting driver actions early and
accurately can help mitigate the effects of potentially unsafe driving
behaviors and avoid possible accidents. In this paper, we formulate driver
action prediction as a timeseries anomaly prediction problem. While the anomaly
(driver actions of interest) detection might be trivial in this context,
finding patterns that consistently precede an anomaly requires searching for or
extracting features across multi-modal sensory inputs. We present such a driver
action prediction system, including a real-time data acquisition, processing
and learning framework for predicting future or impending driver action. The
proposed system incorporates camera-based knowledge of the driving environment
and the driver themselves, in addition to traditional vehicle dynamics. It then
uses a deep bidirectional recurrent neural network (DBRNN) to learn the
correlation between sensory inputs and impending driver behavior achieving
accurate and high horizon action prediction. The proposed system performs
better than other existing systems on driver action prediction tasks and can
accurately predict key driver actions including acceleration, braking, lane
change and turning at durations of 5sec before the action is executed by the
driver.