Long short term memory for driver intent prediction
Modern advanced driver assistance systems (ADAS) have lead to safer vehicles. However, current ADAS are typically limited to a reactive, physical model of the vehicle. They lack the ability to understand complex traffic scenarios. One traffic scenario that has gathered interest in recent years is the problem of inferring driver behaviour at road features such as intersections. At these locations drivers may choose to perform one of many available manoeuvres. Early identification of the manoeuvre is important for the development of future safety and situational awareness systems. The objective of this paper is to develop a method for predicting which manoeuvre a driver will execute. To fulfil this objective a simple method based on quadratic discriminant analysis is proposed. The method is computationally efficient and developed with a view to being applied to complex road networks using naturalistic driving data. The proposed method is demonstrated and validated using naturalistic driving data collected at a three way T-intersection.