Connected and Autonomous Vehicles Trajectory Optimization for an On-Ramp Freeway Merging Segment in a Mixed Vehicular Traffic Environment

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Efficient and smooth merging processes on highways are critical for ensuring traffic safety, flow, and network efficiency. While traditional techniques, such as ramp metering and variable speed limits, offer benefits, their ability to optimize highway throughput remains limited. The emergence of connected and automated vehicles (CAVs) in road networks holds promise for enhancing transportation network efficiency and safety. This research develops a novel control algorithm focused on optimizing autonomous vehicle trajectories in a mixed traffic environment on a multilane highway. The objective is to eliminate stop-and-go conditions during merging and enhance a smooth and safe merging maneuver. The merging process is outlined in a hierarchical hybrid control framework that consists of three control layers: the top control layer, the intermediate tactical control layer, and the lower operational layer. At the top level, the controller gathers data from the real-time traffic environment and identifies a group of vehicles most impacted by the merging maneuvers. This information is relayed to the mid-layer, which then establishes a multiphase kinematic model of the system. Utilizing this model, the tactical controller designs a multi-input-multi-output (MIMO) model predictive control (MPC) scheme that optimizes autonomous vehicle trajectories while adhering to various constraints. At the operational level, CAVs employ optimized trajectories as reference signals for executing essential longitudinal and lateral maneuvers during merging operations. A PI (Proportional-Integral) control scheme regulates longitudinal maneuvers, while a PID (Proportional-Integral-Derivative) control scheme manages lateral maneuvers, and both schemes consider the distinctive vehicle dynamics of each CAV. Through comprehensive simulations that encompass diverse driving scenarios, the hybrid technique demonstrates reliability, robustness, and precision across variable initial conditions. This study also introduces a novel centralized control technique that integrates the control layers of the hybrid control system into a single layer and manages the entire merging process in a continuous motion. Comparing the hybrid and centralized control techniques demonstrates that the hybrid approach has remarkable computational efficiency and showcases higher robustness against model uncertainties and communication disturbances. In contrast, the centralized controller exhibits better stability and control performance with higher fuel efficiency and passenger comfort.
Merging Control, Connected and Autonomous Vehicles, Human-Driven Vehicles, Multilane Freeway, Mixed Traffic
Hesabi Hesari, A. (2024). Connected and autonomous vehicles trajectory optimization for an on-ramp freeway merging segment in a mixed vehicular traffic environment (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from