Introducing the Effects of Road Geometry Into Microscopic Traffic Models for Automated Vehicles

Abstract

Road geometry (e.g. slope and curvature) has significant impacts on driving behaviours of low-level automated vehicles (AVs), but it has been largely ignored in microscopic traffic models. To capture these effects, this study proposes a generic approach to extend any (free-flow or car-following) microscopic models characterized by acceleration functions. To this end, three model extensions are developed, each of which can use different submodels for comparison. Their effectiveness is demonstrated with a microscopic free-flow model that represents the AVs' control logic. Finally, all possible combinations of the microscopic model and the model extensions are calibrated and cross-validated against data sets, which contain empirical trajectories of commercial adaptive cruise control (ACC) systems on different test tracks. Results suggest that two submodels, i.e., the nonlinear vehicle dynamics (NVD) and the radius difference method (RDM), can extend the microscopic model to effectively capture the effects of road slope and road curvature, respectively, on automated driving. Specifically, the NVD is the dominant factor contributing to increasing (by 34.9% on average) model accuracy. In addition, when simulating reckless turning behaviours (i.e. the vehicle turns at high speeds), the inclusion of the developed RDM is significant for model performance, and the models extended with both the NVD and the RDM can achieve the largest accuracy gains (39.6%).

Examining the feasibility of current spiral curve design controls for LiDAR‐based automated vehicles

“…dense fog, heavy rain) [82]. Besides, the actual trajectories and driving speeds of LAVs under the same geometry conditions investigated in this study might not be as ideal as those adopted in this study, which could be important for analyzing ASDs in safety‐critical scenarios [83, 84]. Therefore, in future investigations: (1) extensive tests considering vertical curves, complex combined geometry, and adverse weather conditions should be conducted; and (2) the preset driving path and speed should be adjusted based on field‐tested matched data of AV trajectory and roadway geometry.…”

Microscopic Modelling of Car-Following Behaviour: Developments and Future Directions

“…These models consider the deterministic characteristics of the traffic, such as speed, flow, and density. On the other hand, microscopic simulation models record the behaviour of individual vehicles for each simulation time step based on car-following, lane-changing, and gap-acceptance algorithms [7,8]. They are used to model complex urban street network systems, interchanges, pedestrian movements, traffic signals, multi-modal systems, and proposed mitigations for existing network problems.…”

Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles Using Spring Damping Energy Model

“…Numerous experimental results have shown that ACC has a negative impact on the traction energy consumption of the vehicle [25], [26]. In addition, more real vehicle experiments and empirical data are needed to support the improvement and optimization of the ACC model [27]. To overcome this issue, some preliminary results have been obtained by researchers in recent years.…”