Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

Abstract

Lane-changing is one of the complex driving tasks that depends on the number of vehicles, objectives, and lanes. A driver often needs to respond to a lane-changing request of a lane-changer, which is a function of their personality traits and the current driving conditions. A connected environment is expected to assist during the lane-changing decision-making process by increasing situational awareness of surrounding traffic through vehicle-to-vehicle communication and vehicle-to-infrastructure communication. Although lane changing decision making process in a traditional environment (an environment without driving aids) has been frequently investigated, our understanding of drivers' interactions during the lane-changing decisionmaking process in a connected environment remains elusive due to the novelty of the connected environment and the scarcity of relevant data. As such, this study examines drivers' responses to lane-changing requests in a connected environment using the CARRS-Q Advanced Driving Simulator. Seventy-eight participants responded to the lane-changing request of a lanechanger under two randomised driving conditions: baseline (traditional environment without driving aids) and connected environment (with driving aids). A segmentation-based approach is employed to extract drivers' responses to the lanechanging request and subsequently estimate their response time from trajectory data. Additionally, drivers' response times are modelled using a random parameter accelerated failure time (AFT) hazard-based duration model. Results reveal that drivers tend to be more cooperative in response to a lane-changing request in the connected environment compared with the baseline condition whereby they tend to accelerate to avoid the lane-changing request. The AFT model suggests that on average drivers' response times are shorter in the connected environment, implying that drivers respond to the lane-changing request faster in the presence of driving aids. However, at the individual level, connected environment's impact on drivers' response times is mixed as drivers' response times may increase or decrease in the connected environment compared to the baseline condition, for instance, we find that female drivers have lower response times in the connected environment than that of male drivers. Overall, this study finds that drivers in connected environment, on average, take less time to respond and appear to be more cooperative, and thus, are less likely to be engaged in safety-critical events.

Off-Ramp Vehicle Mandatory Lane-Changing Duration in Small Spacing Section of Tunnel-Interchange Section Based on Survival Analysis

“…Ali et al [14] used a parametric accelerated failure time hazard-based duration model to study the minimum gap time between the interacting vehicles during the mandatory lane-changing. Duration is another important characteristic of lane-changing behavior [15][16][17]. Studies have demonstrated that the duration of a lane change roughly ranges from 0.5 s to 16 s and obeys a normal distribution [18].…”

Lane‐Changing Trajectory Prediction Modeling Using Neural Networks

“…Additionally, drivers' response times are modeled using a random parameter accelerated failure time (AFT) hazardbased duration model. Results revealed that drivers tend to be more cooperative in response to a lane-changing request in the connected environment compared with the baseline condition whereby they tend to accelerate to avoid the lanechanging request [25]. Hence, a large number of works were conducted to focus on the two-dimensional prediction of trajectories for the driving behavior mimicking of humans [26][27][28][29][30].…”

Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

“…e autonomous vehicle (AV) is able to detect its surroundings, identify obstacle, and signages that, in turn, enable it to develop its navigation paths and move along the road network without human control. Current AV research projects have focused on developing AV technologies to improve vehicle safety, particularly when performing its fundamental tasks, including car following, lane-keeping, and lane-changing [1][2][3][4][5][6]. Due to its complex process, which involves interactions of host vehicles and its following and leading vehicles, little attention has been given to the AV lane change prediction model.…”

“…Due to its complex process, which involves interactions of host vehicles and its following and leading vehicles, little attention has been given to the AV lane change prediction model. e existing lane-change algorithm (e.g., trajectory planning and maneuver planning algorithm) focused on maximizing individual vehicles' benefit [1][2][3][4][5][6]. In addition to that, these algorithms required detailed microscopic traffic variables (i.e., relative speed and positions) of the surrounding subject vehicles, gaps between the host and following and leading vehicles, which often depends not only on the behavior and movement of the surrounding vehicles but also on the macroscopic traffic dynamics.…”