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A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers
Pin Wang, Ching-Yao Chan, Arnaud de La Fortelle
TL;DR
Automated lane changing must handle interactive, sequential driving situations that predefined rules and models may not cover well. The paper trains a reinforcement-learning agent with continuous states and actions, using a quadratic Q-function with a closed-form greedy policy; simulations show preliminary learning of the lane-change policy and convergence of losses and rewards.
Problem
Automated lane changing requires operational control amid interactive and unforeseen situations, while predefined approaches may be limited outside their designed conditions.
Method
The paper trains a reinforcement-learning agent with continuous state and action spaces, using a quadratic Q-function approximator with a closed-form greedy action.
Results
Simulation training showed converging loss and reward trends, indicating that the RL agent can learn the lane-change policy under the proposed reward and Q-function architecture.
Takeaways & Limitations
The results provide a preliminary indication that reinforcement learning can learn smooth and efficient automated lane-change behavior in interactive driving environments.
Abstract
from arXiv · showhide
Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we proposed a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treated both state space and action space as continuous, and designed a Q-function approximator that has a closed- form greedy policy, which contributes to the computation efficiency of our deep Q-learning algorithm. Extensive simulations are conducted for training the algorithm, and the results illustrate that the Reinforcement Learning based vehicle agent is capable of learning a smooth and efficient driving policy for lane change maneuvers.
I. INTRODUCTION
Lane changing is a crucial transition maneuver for automated vehicles, and this paper focuses on using reinforcement learning to learn operational control policies for it. The introduction frames lane changes as sequential decisions where current actions affect successful completion.
- Lane changing is a crucial function for ADAS and fully automated vehicles, especially as research shifts toward riskier transition maneuvers.
- The paper focuses on operational control: automatically performing a lane change after receiving a command from a decision-making module.
- Lane changing is a time-sequential task in which current actions influence the eventual success of the maneuver.
- Reinforcement learning is applied to find an optimal driving policy for smooth, safe, and efficient automated lane changes.
II. RELATED WORK
Earlier lane-change approaches rely on predefined trajectories, models, or limited data-driven components, which can constrain flexibility in dynamic and unforeseen situations. This work extends reinforcement learning to interactive lane-change control with continuous states and actions.
- Traditional lane-change methods use predefined rules, trajectories, or waypoints, limiting flexibility under dynamic situations and conditions outside model assumptions.
- Model predictive control may struggle when optimization criteria are too complex to formulate across all scenarios and future trajectories must be predicted.
- Earlier automated-driving machine-learning studies included lane-change decisions, MPC execution, or adaptive lateral control, but some required difficult-to-obtain optimal acceleration inputs.
- Previous reinforcement-learning driving studies considered relatively simple scenarios because they included few interactions with surrounding vehicles.
- The authors extend prior RL work on interactive ramp merging by addressing lateral control and expanding the state, action, and reward definitions for lane changes.
III. METHODOLOGY
The methodology separates lane-change control into coordinated modules while using reinforcement learning for continuous lateral control. The state and reward designs incorporate vehicle dynamics, road geometry, smoothness, and efficiency.
- 3.1 Lane Change Controllers: The system uses separate longitudinal and lateral controllers, with a gap-selection module operating alongside them.The longitudinal controller leverages a modified IDM, while lateral control is learned by reinforcement learning.
- 3.1 Lane Change Controllers: The gap-selection module checks target-lane gaps using surrounding vehicles’ speeds, accelerations, positions, and safety-distance constraints.A gap is acceptable when it accommodates speed differences under allowable acceleration or deceleration and maintains a minimum safety distance.
- 3.3.1 Action Space: The lateral controller learns continuous yaw acceleration because abrupt steering changes can produce erratic yaw-rate changes and lane disturbance.The action space is therefore defined using vehicle yaw acceleration rather than discrete steering commands.
- 3.3.2 State Space: The state space combines ego-vehicle dynamics with road information, including speed, longitudinal acceleration, position, yaw angle, target lane, lane width, and curvature.The formulation assumes these inputs are available from sensor-fusion modules and can be expanded for high-dimensional or noisy perception inputs.
- 3.3.3 Reward Function: The reward function targets smoothness and efficiency through yaw acceleration, yaw rate, and lane-changing time, while safety is handled by the longitudinal and gap-selection modules.Reward components are selected according to their relevance to action performance, with weights tuned across parameter settings.
- 3.3.4 Q-learning: The Q-function is designed to be quadratic in action, giving the greedy action a closed-form solution without a separate policy neural network.The coefficients are produced from state information by neural networks, supporting Q-learning with continuous actions.
IV. SIMULATION AND RESULT
The RL agent was trained in a customizable three-lane highway simulation, where decreasing loss and increasing rewards indicated convergence and learning of the lane-change policy.
- Simulation Environment: The simulation used a 1000m, three-lane highway with 3.75m lanes and customizable traffic conditions, including randomized departure intervals and speed limits.Departure intervals ranged from 5s-10s, while individual speed limits ranged from 80km/h-120km/h.
- Training Setup: 40,000 training steps produced approximately 5000 lane-change maneuvers, using a 0.1s time step, learning rate 0.01, and discount factor 0.9.
- Training Results: The loss curve showed an obvious downtrend, indicating convergence along with the training steps.
- Training Results: The total reward increased during training, while the other three reward graphs showed similar increasing trends as supplementary evidence of learnability.
- Training Results: The training results preliminarily indicated that the RL agent could learn a lane-change policy under the designed reward function and Q-function architecture.Further evaluation of kinematic driving performance was identified as future work.
V. CONCLUSION AND DISCUSSION
The study applied continuous-space reinforcement learning to automated lane changing and used a simulation platform with diverse traffic scenarios. Preliminary convergence results were promising, while broader robustness and performance validation remained future work.
- Conclusion: The approach used reinforcement learning with continuous state and action spaces to learn automated lane-change behavior in an interactive driving environment.
- Conclusion: A quadratic Q-function approximator with neural-network-learned coefficients was used, and rewards incorporated yaw rate, yaw acceleration, and lane-changing time.
- Conclusion: The simulation platform generated diverse scenarios by adjusting traffic density, initial speeds, and speed limits.
- Conclusion: Preliminary training results showed convergence of the loss and rewards defined in the learning framework, indicating a promising attempt.
- Future Work: Future work will test different road geometries and traffic-flow conditions and compare lane-changing performance with optimization-based approaches such as MPC.The paper also proposes combining RL with MPC and extending inputs to image-based state information.