The impact of the following vehicles behaviors on the car following behaviors of the ego-vehicle

Abstract: Among all types of crashes, rear-end crashes dominate, which are closely related to the car-following (CF) behaviors. Traditional CF behavior models focused on the influence of the vehicle in front, but usually ignored the peer pressure from the surrounding road users, including the following vehicle (FV). Based on an open dataset, the highD dataset, we investigated whether the FV's states can affect the CF behavior of the ego-vehicle in CF events. Two types of CF events were extracted from highD database, including the tailgated events, where the time headway between the FV and the ego-vehicle (i.e., time gap) was smaller than 1 second, and the gapped events, where the time gap was larger than 3 seconds. The dynamic time warping was used to extract CF pairs with similar speed profiles of the leading vehicle (LV). Statistical analyses were conducted to compare the CF-performance metrics in tailgated and gapped events. Then, the inverse reinforcement learning was used to recover the reward function of the ego-vehicle drivers in different CF events. The results showed that the ego-driver would adjust their CF behavior in response to the pressure from a tailgating FV, by maintaining a closer distance to the LV, but at the same time, driving more cautiously. Further, drivers were still able to adjust their CF strategies based on the speed of traffic flow and the distance to the LV, even when being tailgated. These findings provide insights regarding more accurate modelling of traffic flow by considering the peer pressure from surrounding road users.