Design of optimal cruise control considering road and traffic information

Németh, Balázs; Csikós, Alfréd; Gáspár, Péter; Varga, István · 2013 · Crossref

DOI: 10.3182/20130204-3-fr-2033.00178

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Summary

This paper presents a design for an optimal cruise control system that integrates look-ahead road and traffic information to minimize longitudinal energy consumption, fuel usage, and engine emissions without significantly increasing travel time. The research addresses the limitations of standard Adaptive Cruise Control (ACC) systems, which react to immediate environmental disturbances but fail to utilize predicted data such as upcoming road inclinations, speed limits, traffic lights, and preceding vehicles. By incorporating these factors, the proposed system aims to reduce unnecessary braking and acceleration events, thereby lowering brake wear and kinetic energy loss. The methodology employs a multi-criteria optimization framework combined with robust $H_\infty$ control to ensure stability against disturbances. The vehicle’s route is divided into sections, with reference velocities defined at endpoints based on speed limits and road slopes. The control strategy utilizes prediction weights ($Q$, $\gamma_i$, and $W$) to balance momentary speed, predicted future speeds, and the speed of preceding vehicles. A key novelty is the integration of traffic light scheduling information via vehicle-infrastructure communication (e.g., infrared detectors or roadside beacons). The system applies a decision logic based on the distance to the intersection, the current signal state, and the remaining time of the signal to adjust speed trajectories. For instance, if a vehicle can reach an intersection during a green light, it maintains or adjusts speed accordingly; if a red light is imminent, the system prioritizes a smooth deceleration to stop, reducing the weight assigned to road slope predictions in favor of stopping maneuvers. The optimization process balances three conflicting objectives: minimizing longitudinal control force, minimizing travel time, and minimizing total emissions. Emissions are modeled using convex rational functions of average vehicle speed for pollutants including CO, CO2, NOx, and hydrocarbons. The system calculates optimal prediction weights by solving nonlinear optimization problems for each criterion and then combines them using performance weights ($R_1$, $R_2$, $R_3$) to achieve a tradeoff. The robust $H_\infty$ controller is then designed to track the resulting optimal speed trajectory. The significance of this work lies in its ability to apply look-ahead control in both highway and urban environments by handling complex traffic signals alongside road geometry. By proactively adjusting speed based on anticipated traffic light states and road conditions, the system reduces the frequency of stop-and-go cycles, which are major contributors to fuel consumption and emissions. The approach demonstrates that integrating infrastructure data into vehicle control logic can significantly improve energy efficiency and environmental performance while maintaining robust stability.

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• speed choice