Long short term memory for driver intent prediction

Abstract

Advanced Driver Assistance Systems have been shown to greatly improve road safety. However, existing systems are typically reactive with an inability to understand complex traffic scenarios. We present a method to predict driver intention as the vehicle enters an intersection using a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). The model is learnt using the position, heading and velocity fused from GPS, IMU and odometry data collected by the ego-vehicle. In this paper we focus on determining the earliest possible moment in which we can classify the driver's intention at an intersection. We consider the outcome of this work an essential component for all levels of road vehicle automation.