Efficiency-increasing driver assistance at signalized intersections using predictive traffic state estimation

Frequent necessary stops at red traffic signals and related braking and acceleration processes significantly affect the fuel consumption and emission rates of a vehicle. The efficiency-increasing potential (fuel saving potential) of a predictive driver assistance system proposing an intelligent vehicle speed adaption well in advance the intersection is examined by a traffic flow model-based simulation. The considered predictive speed assistance system is based on the transmission of traffic light controller information into the approaching vehicle. Besides information on the traffic light timing chart, provision of accurate information on current traffic conditions between the current position of the vehicle and the stop line are necessary for a majority of driving situations. Here, the use of queue length estimation (QLE) techniques based on commonly installed induction loop sensor systems is described to extend the functional benefit of the driver assistance system. From QLE data, two additional main indicators (distance to virtual stop line and time to cleared intersection) can be derived to calculate a energy-efficient speed profile. For a single vehicle approaching an isolated intersection signalized with a standard timing cycle fuel savings of 8-11% can be found. The benefit of a QLE-included control scheme of the assistance system is demonstrated by simulation. Simulation results show situation-specific fuel saving potential differences of up to 28% compared to a basic system control scheme (only traffic light timing information).