1,582 research outputs found
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
Autonomous driving has attracted significant research interests in the past
two decades as it offers many potential benefits, including releasing drivers
from exhausting driving and mitigating traffic congestion, among others.
Despite promising progress, lane-changing remains a great challenge for
autonomous vehicles (AV), especially in mixed and dynamic traffic scenarios.
Recently, reinforcement learning (RL), a powerful data-driven control method,
has been widely explored for lane-changing decision makings in AVs with
encouraging results demonstrated. However, the majority of those studies are
focused on a single-vehicle setting, and lane-changing in the context of
multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce
attention. In this paper, we formulate the lane-changing decision making of
multiple AVs in a mixed-traffic highway environment as a multi-agent
reinforcement learning (MARL) problem, where each AV makes lane-changing
decisions based on the motions of both neighboring AVs and HDVs. Specifically,
a multi-agent advantage actor-critic network (MA2C) is developed with a novel
local reward design and a parameter sharing scheme. In particular, a
multi-objective reward function is proposed to incorporate fuel efficiency,
driving comfort, and safety of autonomous driving. Comprehensive experimental
results, conducted under three different traffic densities and various levels
of human driver aggressiveness, show that our proposed MARL framework
consistently outperforms several state-of-the-art benchmarks in terms of
efficiency, safety and driver comfort.Comment: This paper was published on Autonomous Intelligent Systems (Volume 2,
article number 5, 2022
Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
Autonomous lane-change, a key feature of advanced driver-assistance systems,
can enhance traffic efficiency and reduce the incidence of accidents. However,
safe driving of autonomous vehicles remains challenging in complex
environments. How to perform safe and appropriate lane change is a popular
topic of research in the field of autonomous driving. Currently, few papers
consider the safety of reinforcement learning in autonomous lane-change
scenarios. We introduce safe hybrid-action reinforcement learning into
discretionary lane change for the first time and propose Parameterized Soft
Actor-Critic with PID Lagrangian (PASAC-PIDLag) algorithm. Furthermore, we
conduct a comparative analysis of the Parameterized Soft Actor-Critic (PASAC),
which is an unsafe version of PASAC-PIDLag. Both algorithms are employed to
train the lane-change strategy of autonomous vehicles to output discrete
lane-change decision and longitudinal vehicle acceleration. Our simulation
results indicate that at a traffic density of 15 vehicles per kilometer (15
veh/km), the PASAC-PIDLag algorithm exhibits superior safety with a collision
rate of 0%, outperforming the PASAC algorithm, which has a collision rate of
1%. The outcomes of the generalization assessments reveal that at low traffic
density levels, both the PASAC-PIDLag and PASAC algorithms are proficient in
attaining a 0% collision rate. Under conditions of high traffic flow density,
the PASAC-PIDLag algorithm surpasses PASAC in terms of both safety and
optimality
Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment
Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster RCNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.publishedVersio
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Connected and automated vehicles (CAVs) have emerged as a potential solution
to the future challenges of developing safe, efficient, and eco-friendly
transportation systems. However, CAV control presents significant challenges,
given the complexity of interconnectivity and coordination required among the
vehicles. To address this, multi-agent reinforcement learning (MARL), with its
notable advancements in addressing complex problems in autonomous driving,
robotics, and human-vehicle interaction, has emerged as a promising tool for
enhancing the capabilities of CAVs. However, there is a notable absence of
current reviews on the state-of-the-art MARL algorithms in the context of CAVs.
Therefore, this paper delivers a comprehensive review of the application of
MARL techniques within the field of CAV control. The paper begins by
introducing MARL, followed by a detailed explanation of its unique advantages
in addressing complex mobility and traffic scenarios that involve multiple
agents. It then presents a comprehensive survey of MARL applications on the
extent of control dimensions for CAVs, covering critical and typical scenarios
such as platooning control, lane-changing, and unsignalized intersections. In
addition, the paper provides a comprehensive review of the prominent simulation
platforms used to create reliable environments for training in MARL. Lastly,
the paper examines the current challenges associated with deploying MARL within
CAV control and outlines potential solutions that can effectively overcome
these issues. Through this review, the study highlights the tremendous
potential of MARL to enhance the performance and collaboration of CAV control
in terms of safety, travel efficiency, and economy
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs),
especially in mixed traffic where AVs coexist with human-driven vehicles
(HDVs). In this paper, we formulate the mixed-traffic highway on-ramp merging
problem as a multi-agent reinforcement learning (MARL) problem, where the AVs
(on both merge lane and through lane) collaboratively learn a policy to adapt
to HDVs to maximize the traffic throughput. We develop an efficient and
scalable MARL framework that can be used in dynamic traffic where the
communication topology could be time-varying. Parameter sharing and local
rewards are exploited to foster inter-agent cooperation while achieving great
scalability. An action masking scheme is employed to improve learning
efficiency by filtering out invalid/unsafe actions at each step. In addition, a
novel priority-based safety supervisor is developed to significantly reduce
collision rate and greatly expedite the training process. A gym-like simulation
environment is developed and open-sourced with three different levels of
traffic densities. We exploit curriculum learning to efficiently learn harder
tasks from trained models under simpler settings. Comprehensive experimental
results show the proposed MARL framework consistently outperforms several
state-of-the-art benchmarks.Comment: 15 figure
Realistic Speed Control of Agents in Traffic Simulation
Agents in multi-agent traffic simulation tend to be more dependent on the rules and existing instructions to move mechanically and unnaturally imitating human behaviors. The agents will not accelerate or decelerate as humans do. Humans have an irregular pattern of acceleration and deceleration when it comes to real-time driving. This includes hitting breaks when not necessary and sometimes even driving above the speed limit to catch up. In prior works, other factors such as drag and simulation-specific parameters were not considered in the models. Additionally, the models were not tested on the traffic simulation frameworks like SUMO. Instead, they utilized simple numerical models to simulate the environment and evaluate the performance of the models. Therefore, there is a need to further investigate and incorporate these additional factors, as well as validate the models on the SUMO platform, to enhance the realism and applicability of the research. It is also difficult to calibrate SUMO to a given traffic scenario as traffic engineers might need to specify manually the vehicle specifications while designing the experiments. It would be easier for engineers to populate the road network with pre-trained agents that require minimal tuning which includes specifying maximum acceleration, deceleration, and minimum and maximum speed of the vehicles to be simulated. We propose a unified system for agents to decide when to accelerate and decelerate with the help of deep reinforcement learning aided by a combination of factors such as instantaneous speed, time, and other important metrics. The proposed system will aid the agents to behave more like humans by acting based on the surrounding agents in complex situations. This in turn can help create a diverse traffic flow that can mimic real-life traffic scenarios
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