Modeling Slow Lead Vehicle Lane Changing
Driving field experiment data were used to investigate lane changes in which a slow lead vehicle was present to:
- characterize lane changes,
- develop predictive models,
- provide collision avoidance system (CAS) design guidelines.
A total of 3,227 slow lead vehicle lane changes over 23,949 miles were completed by sixteen commuters. Two instrumented vehicles, a sedan and an SUV, were outfitted with video, sensor, and radar data systems that collected data in an unobtrusive manner.
Results indicate that 37.2% of lane changes are slow lead vehicle lane changes, with a mean completion time of 6.3 s; most slow lead vehicle lane changes are leftward, rated low in urgency and severity. A stratified sample of 120 lane changes was selected to include a range of maneuvers. On the interstate, lane changes are performed less often, t(30) = 2.83, p = 0.008, with lower urgency ratings, F(1, 31) = 5.24, p = 0.05, as compared to highway lane changes, as interstates are designed for smooth flow. Drivers who usually drive sedans are more likely to make lane changes than drivers of SUVs, X ²⁺(1)= 99.6247, p < 0.0001, suggesting that driving style is maintained regardless of which experimental vehicle is driven.
Turn signals are used 64% of the time but some drivers signal after the lane change starts. Of cases in which signals are not used, 70% of them are made with other vehicles nearby. Eyeglance analysis revealed that the forward view, rearview mirror, and left mirror are the most likely glance locations. There are also distinct eyeglance patterns for lane changing and baseline driving.
Recommendations are to use forward view or mirror-based visual displays to indicate presence detection, and auditory displays for imminent warnings.
The "vehicle + signal" logistic regression model is best overall since it takes advantage of the distance to the front and rear adjacent vehicle, forward time-to-collision (TTC), and turn signal activation. The use of additional regressors would also improve the model. Five design guidelines are included to aid in the development of CAS that are useable, safe, and integrated with other systems, given testing and development.