Optimal Trajectory Planning for Connected and Automated Vehicles in Lane-Free Traffic With Vehicle Nudging

Yanumula, Venkata Karteek; Typaldos, Panagiotis; Troullinos, Dimitrios; Malekzadeh, Milad; Papamichail, Ioannis; Papageorgiou, Markos · 2023 · OpenAlex-citations

DOI: 10.1109/tiv.2023.3241200

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Summary

This paper addresses the challenge of optimizing traffic flow and safety in lane-free environments for Connected and Automated Vehicles (CAVs). The authors argue that traditional lane-based driving underutilizes road infrastructure and causes congestion, whereas lane-free driving allows CAVs to utilize lateral space more efficiently. The research proposes a movement strategy involving "vehicle nudging," where vehicles influence each other’s trajectories not just longitudinally but also laterally, to improve traffic capacity and stability. The study formulates a nonlinear constrained Optimal Control Problem (OCP) within a Model Predictive Control (MPC) framework. Each vehicle is modeled using discrete-time double integrators for longitudinal and lateral dynamics. The objective function minimizes a weighted sum of sub-objectives, including fuel consumption, passenger comfort (via smooth acceleration), adherence to desired speeds, and obstacle avoidance. To ensure safety, the authors design state-dependent constraints on control inputs. These constraints prevent collisions and keep vehicles within road boundaries by defining imaginary ellipsoid hemispheres around obstacles and using feedback controllers to enforce lateral and longitudinal bounds. The OCP is solved in real-time using a computationally efficient Feasible Direction Algorithm (FDA) on an event-triggered basis, with an average computation time of 12.4 ms per 8-second planning horizon. Simulations were conducted on a lane-free ring-road with hundreds of vehicles across a wide range of densities. The results demonstrate that the proposed approach generates safe, comfortable, and efficient trajectories. At the traffic level, the simulations reveal a fundamental diagram with a typical inverse-U shape, indicating stable flow dynamics. Crucially, the emerging flow values were significantly higher than those observed in conventional lane-based traffic. The vehicles maintained smooth movement and passenger convenience across all tested densities, effectively utilizing the available road width without the discontinuous lateral displacements associated with lane changes. The significance of this work lies in its demonstration that lane-free driving with vehicle nudging can drastically alleviate safety and congestion issues compared to human-inspired driving restrictions. By relaxing lane structures and employing optimal control algorithms, CAVs can achieve higher traffic capacity and efficiency. The study provides a robust numerical solution for real-time application, suggesting that future traffic infrastructure dominated by automated vehicles could benefit from abandoning fixed lanes in favor of fluid-like, continuous trajectory planning. This approach offers a pathway to maximizing road usability and improving overall transportation system performance.

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