Effects of using Motion Predictions in Automated Driving in Highway Lane Merging

Automating the driving task could improve traffic safety, since 93% of traffic accidents are human failure related. To enable automated driving, one of the challenges is to plan safe and optimal trajectories. Previous research mentions that future road users' predictions are a prerequisite to achieve safe and high-quality motion planning for autonomous driving. This is a possible claim, yet no research exists that explains to what extend the availability prediction is useful. To quantifiable investigate the claim that other road users' predictions indeed result in safer and comfortable traffic, in this paper a safety and comfort analysis of different highway scenarios is performed for different levels of automation. Our simulation-based analysis shows that the availability of other road users' predictions indeed leads to increased safety in different lane merging scenarios (i.e., merging and traffic jam; short and long ramps). Further, the effect of predictions is more visible in higher level automated cars. However, comfort improvements with predictions was not witnessed in the data, due to a balance between comfort and safety (i.e., cases that become safe are not necessarily comfortable). Less comfortable, but safe, faster trajectories are preferred during the simulation with predictions (i.e., trajectories with higher velocity and accelerations).