[Paper Review] Design and Implementation of Ecological Adaptive Cruise Control for Autonomous Driving with Communication to Traffic Lights

Sangjae Bae, Yeojun Kim+3 more|arXiv (Cornell University)|Oct 29, 2018

Traffic control and management10 references4 citations

TL;DR

This paper presents an Eco-ACC controller that integrates ecological driving with adaptive cruise control using real-time traffic light phase and timing (SPaT) data to minimize fuel consumption while avoiding collisions and red-light violations. By combining chance-constrained optimal control for green-wave coordination with a model predictive control-based ACC, the system achieves 41% fuel savings in real-world testing on a 2.6 km urban route with eight signalized intersections.

ABSTRACT

This paper presents the design and implementation results of an ecological adaptive cruise controller (ECO-ACC) which exploits driving automation and connectivity. The controller avoids front collisions and traffic light violations, and is designed to reduce the energy consumption of connected automated vehicles by utilizing historical and real-time signal phase and timing data of traffic lights that adapt to the current traffic conditions. We propose an optimization-based generation of a reference velocity, and a velocity-tracking model predictive controller that avoids front collisions and violations. We present an experimental setup encompassing the real vehicle and controller in the loop, and an environment simulator in which the traffic flow and the traffic light patterns are calibrated on real-world data. We present and analyze simulation and experimental results, finding a significant potential for energy consumption reduction, even in the presence of traffic.

Motivation & Objective

• Address the gap in experimental validation of eco-driving controllers in real-world traffic conditions with connected vehicle technology.
• Integrate energy-efficient eco-driving with safety-focused adaptive cruise control to balance fuel economy and collision avoidance.
• Develop a real-time control framework that leverages historical and real-time SPaT data to optimize velocity trajectories through signalized intersections.
• Overcome limitations of offline planning by implementing an online, receding-horizon controller robust to traffic-induced delays.
• Validate the controller in a hardware-in-the-loop setup using real vehicle and traffic data from Arcadia, CA.

Proposed method

• Formulate an eco-driving problem using chance-constrained optimal control to handle uncertainty in effective red light durations.
• Design a model predictive control (MPC) framework for the ACC layer to ensure collision avoidance and traffic light compliance.
• Implement a two-layer control architecture: high-level Eco-driving controller generates a reference velocity profile, and low-level ACC tracks it.
• Use real-world SPaT data from Sensys Networks to calibrate traffic light patterns and traffic flow in a high-fidelity simulator.
• Deploy the controller in a hardware-in-the-loop setup with a plug-in hybrid electric vehicle, integrating real-time vehicle dynamics and communication with a traffic signal simulator.
• Apply dynamic programming to precompute optimal velocity profiles offline and compare with online receding-horizon solutions.

Experimental results

Research questions

• RQ1 Can an online Eco-ACC controller achieve significant fuel savings while ensuring safety and traffic signal compliance in real urban traffic?
• RQ2 How does the performance of online versus offline eco-driving planning differ under free-flow and congested traffic conditions?
• RQ3 To what extent does uncertainty in red light duration affect the reliability of green-wave coordination strategies?
• RQ4 How does the integration of real-time SPaT data and adaptive cruise control improve energy efficiency compared to conventional ACC?
• RQ5 What are the practical limitations of offline eco-driving planning when faced with unpredictable traffic delays?

Key findings

• The ECO-ACC controller achieved a 41% reduction in fuel consumption compared to a conventional ACC-only controller on a 2.6 km urban route with eight signalized intersections.
• In free-flow conditions, online and offline eco-driving controllers produced nearly identical velocity profiles, confirming the validity of offline planning under ideal conditions.
• With traffic present, the online controller adapted to delays caused by surrounding vehicles, while the offline controller failed entirely, resulting in the vehicle becoming stranded.
• The mean and standard deviation of vehicle speed were reduced by 0.73 m/s and 1.16 m/s, respectively, under ECO-ACC, indicating smoother and more stable driving.
• The velocity profile showed minimal chattering due to the ACC controller’s balancing of distance to front vehicles, while still maintaining green-wave coordination.
• The controller successfully passed through all intersections during green phases, as verified by the simulation and experimental results, with no red-light violations.