Optimization model for the freeway-exiting position decision problem of automated vehicles

In recent years, automated vehicles have attracted much attention all over the world. This paper focuses on the freeway-exiting position decision problem of automated vehicles (AVs). Specifically, the paper addresses the determination of the lane-changing initiation location in the process of exiting the freeway. The location of the freeway-exiting decision point has a significant impact on the safety and efficiency of automated vehicles. If the lane-changing location is too close to the off-ramp, the AV may not succeed in exiting and may even collide with other vehicles. If the decision point is too far from the off-ramp, the AV will enter into the slower lane too early, increasing the travel time. However, the freeway-exiting lane-changing position problem of AVs has not been investigated thoroughly in the existing literature. This paper proposes a freeway-exiting position decision model to find the optimal freeway-exiting decision position to balance the efficiency and safety in the freeway-existing process. Field data is collected to validate the proposed model, and simulations are also conducted to analyze the variations of the exiting success probability (ESP) and the optimal exiting decision (OED) position under various traffic conditions. The results show that the proposed model can predict the value of ESP with high performance (MAPE is less than 13%) and help an automated vehicle to generate an appropriate freeway-exiting decision point to ensure a high ESP without sacrificing efficiency. An AV can increase its ESP by decreasing or increasing its speed to meet more safe lane-changing gaps on the target lane, and the speed-decreasing method has a more significant effect than the speed-increasing method. The speed difference between the two adjacent lanes greatly influences ESP and the OED point, and maintaining the speed difference in an appropriate range can increase ESP.