Network-level comparison of various Forward Collision Warning algorithms

Abstract

Rear-end collisions represent a quarter to one-third of the total number of collisions occurring on North American roads. Consequently, Forward Collision Warning (FCW) algorithms have been developed to mitigate this type of critical collision by warning drivers about an impending rear-end event. The algorithms are typically tested to ensure their effectiveness in reducing specific events, such as rear-end conflicts and/or collisions, or by assessing the change in the frequency and severity of braking maneuvers. Such assessments are usually microscopic in nature and deal with isolated (independent) situations. This paper aims at assessing six FCW algorithms at a network level with varying market penetration rates using a calibrated micro-simulation model. The algorithms were assessed in terms of their safety (rear-end conflicts frequency), mobility (travel times), and environmental impacts (emissions and fuel consumption). Based on the results of this study, most of the FCW algorithms did not have a significant effect on mobility nor environmental impacts at various market penetration rates. On the contrary, all the algorithms showed significant safety improvements, in terms of reducing rear-end conflicts, as the market penetration rates increased. The only exception was a single algorithm that tends to be more conservative in terms of braking distance. The results showed that situational improvements (on a driver level) caused by using FCW systems will generally translate into systematic improvements (on a network level). This is important due to the anticipated gradual increase in intelligent vehicles, which are expected to be equipped with FCW systems, on our roads soon.