Vehicle Position and Context Detection Using V2V Communication

Abstract

A pre-crash detection and warning system in a host vehicle needs to accurately determine the position of each remote vehicle in its vicinity and the context of the driving environment. ADAS (Advanced driver-assistance systems) have extensively used camera radar and LIDAR for automatic detection of vehicles, pedestrians and other road users and their behaviors. However, these vehicle-resident sensors have short operation ranges and require objects to be within the line-of-sight. V2V communication has emerged to be a promising technology to augment vehicle-resident sensors with extended ability of an overall vehicle safety system by addressing a broader range of crash scenarios with improved warning timing. In this paper we present an intelligent system, Geo+NN, developed using the synergy of neural network and geometric modeling. We extract the key geometric features using an analytic geometric model and use them as input to a neural network that is trained on real-world V2V signals to detect and predict remote vehicles’ positions. Geo+NN system is evaluated on V2V communication data recorded during real-world driving trips by vehicles installed with DSRC devices. Experimental results show that Geo+NN has the capabilities of effectively detecting and predicting remote vehicles within the context of 8 different positions.