Personalized Driver Assistance for Signalized Intersections Using V2I Communication

Intersection crossing is a frequent driving maneuver in urban driving. Traffic lights and stop signs force the vehicles to stop and restart causing frustration to drivers. To help the driver in approaching and passing intersections, the vehicle can be equipped with an advanced driver assistance system (ADAS) that utilizes vehicle-to-infrastructure communication. As opposed to centralized traffic light optimization, the in-vehicle system uses information on traffic light location and timing to find an individual optimal driving pace, which is then conveyed to the driver. The conventional approach to the pace optimization problem is to consider such parameters as time of arrival, fuel consumption, and emissions. However, to be effective, the system should take into account another important factor-driver's preferences and driving characteristics to improve the acceptance of the system. In this paper, we propose a personalized pace optimization algorithm for approaching and passing signalized intersections that can be used in ADAS. It learns the personal driver's preferences and characteristics during the course of driving and uses this knowledge to calculate driving pace that improves fuel economy, reduces waiting time, and addresses the driver's preferences. We demonstrate the proposed methodology by comparing the system's recommendations for drivers with different preferences and driving styles. Drivers' features were extracted from data collected on an experimental vehicle. We utilize the drivers' profiles to find an optimal driving pace for a specified route for each particular driver. We validate the methodology by simulation to show fuel economy benefits of the generated speed profiles.