Model predictive control for intelligent speed adaptation in Intelligent Vehicle Highway Systems

Baskar, Lakshmi Dhevi; De Schutter, Bart; Hellendoorn, Hans · 2008 · Crossref

DOI: 10.1109/cca.2008.4629646

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Summary

This paper addresses the challenge of traffic congestion in Intelligent Vehicle Highway Systems (IVHS) by proposing a Model Predictive Control (MPC) framework for Intelligent Speed Adaptation (ISA). The authors aim to optimize traffic flow and minimize the total time spent by vehicles in the network by dynamically assigning speed limits to platoons of fully automated vehicles. The motivation stems from the limitations of current infrastructure and the potential of IVHS to eliminate human reaction delays and errors, thereby increasing throughput through closely spaced platoons. The study employs a hierarchical control structure where roadside controllers determine optimal speed set-points for platoon leaders using MPC. The control problem is formulated as a nonlinear optimization task that minimizes total time spent, subject to constraints on speed, separation distances, and control signal variations. To manage computational complexity, the authors utilize simplified prediction models rather than detailed microscopic simulations. These models include kinematic equations for vehicle dynamics and specific longitudinal behavior models: a proportional controller for platoon leaders following ISA limits, and a combined speed and distance controller for follower vehicles maintaining intraplatoon spacing via Adaptive Cruise Control (ACC). A platoon-based aggregate model treats each platoon as a single entity with speed-dependent length. The proposed approach is validated through a simulation case study of a 15 km single-lane highway stretch. The authors compare three scenarios: uncontrolled human-driven traffic, human-driven traffic with autonomous ISA, and IV-based traffic with platoons controlled by the proposed MPC. The simulation parameters include a 1-minute control sampling interval, a 15-minute prediction horizon, and fixed platoon sizes of five vehicles. The results demonstrate that the MPC-based IVHS approach significantly reduces total time spent compared to human-driven scenarios. Notably, the study highlights that fully automated platoons eliminate the "capacity drop" phenomenon—typically a 2–7% reduction in outflow capacity caused by human reaction delays during congestion—resulting in a capacity drop of approximately 0%. The significance of this work lies in demonstrating the efficacy of MPC for coordinating automated vehicle platoons within a hierarchical IVHS framework. By treating platoons as single entities and using dynamic speed limits, the roadside controller can efficiently manage network-wide traffic performance. The findings suggest that integrating roadside intelligence with automated vehicle control can substantially improve traffic throughput and reduce congestion impacts, offering a viable long-term solution for efficient highway management.

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• speed choice

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• Theoretical Contribution: computational model