A multiple vehicle sensing approach for collision avoidance in progressively deployed vehicle networks

Abstract

Dedicated Short Range Communications (DSRC), a promising vehicle-to-vehicle communication technology, has been under active research and large scale DSRC deployment is expected to start shortly. However, before all vehicles are deployed with DSRC, there will be a relatively long partial DSRC deployment period where DSRC-equipped vehicles and non-DSRC-equipped vehicles both exist on roads. More importantly, it is reported that the probability a DSRC-equipped vehicle will benefit from a safety application is only of 1% during the initial DSRC deployment. Therefore, we propose MVS, a Multiple Vehicle Sensing approach to improve the collision avoidance effectiveness under partial DSRC deployment. The design of MVS is based on the observation that vehicles are able to sense the kinematic states of its adjacent vehicles by using existing computer vision technologies and/or on-board radar technologies. Therefore, we focus on improving the efficiency of sharing these sensed kinematic states among DSRC-equipped vehicles. By using the sensed data from multiple adjacent vehicles, the kinematic states of a non-DSRC-equipped vehicle can be accurately estimated. MVS is implemented and evaluated through a trace-driven study based on two realistic vehicle mobility traces. Results show that MVS reduces the collision probability by 61.5% and 60.1% in the two traces.