Learning Eco-Driving Strategies at Signalized Intersections

  title={Learning Eco-Driving Strategies at Signalized Intersections},
  author={Vindula Jayawardana and Cathy Wu},
—Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO 2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco… 

Figures and Tables from this paper


Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach
An eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection and demonstrates that the algorithm works less effective when the traffic signal offset is closer to its optimal value.
Eco-Cooperative Adaptive Cruise Control at Signalized Intersections Considering Queue Effects
An Eco-CACC algorithm is developed that computes the fuel-optimum vehicle trajectory through a signalized intersection by ensuring that the vehicle arrives at the intersection stop bar just as the last queued vehicle is discharged.
Robust Optimal ECO-driving Control with Uncertain Traffic Signal Timing
The concept of ‘effective red-light duration’ (ERD) is introduced, formulated as a random variable, to describe the feasible passing time through signalized intersections in the face of uncertain signal timing.
Impact of Automated Vehicle Eco-Approach on Human-Driven Vehicles
A driving simulation environment for the mixed traffic to assess the benefits of the eco-approach of CAVs and its impact on surrounding vehicles in the Mixed traffic indicates the energy efficiency benefit.
A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles
The Eco-driving problem is formulated as a Partially Observable Markov Decision Process (POMDP), which is then solved with a state-of-art Deep Reinforcement Learning (DRL) Actor Critic algorithm, Proximal Policy Optimization.
Emergent Behaviors in Mixed-Autonomy Traffic
The present article formulates and approaches the mixed-autonomy traffic control problem using the powerful framework of deep reinforcement learning (RL) to provide insight for the potential for automation of traffic through mixed fleets of automated and manned vehicles.
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
The suitability of deep reinforcement learning (RL) for overcoming challenges in a low AV-adoption regime is studied and a modular learning framework is presented, which leverages deep RL to address complex traffic dynamics.