766 research outputs found
An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
Due to the complexity of the natural world, a programmer cannot foresee all
possible situations, a connected and autonomous vehicle (CAV) will face during
its operation, and hence, CAVs will need to learn to make decisions
autonomously. Due to the sensing of its surroundings and information exchanged
with other vehicles and road infrastructure, a CAV will have access to large
amounts of useful data. While different control algorithms have been proposed
for CAVs, the benefits brought about by connectedness of autonomous vehicles to
other vehicles and to the infrastructure, and its implications on policy
learning has not been investigated in literature. This paper investigates a
data driven driving policy learning framework through an agent-based modelling
approaches. The contributions of the paper are two-fold. A dynamic programming
framework is proposed for in-vehicle policy learning with and without
connectivity to neighboring vehicles. The simulation results indicate that
while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V)
communication of information improves this capability. Furthermore, to overcome
the limitations of sensing in a CAV, the paper proposes a novel concept for
infrastructure-led policy learning and communication with autonomous vehicles.
In infrastructure-led policy learning, road-side infrastructure senses and
captures successful vehicle maneuvers and learns an optimal policy from those
temporal sequences, and when a vehicle approaches the road-side unit, the
policy is communicated to the CAV. Deep-imitation learning methodology is
proposed to develop such an infrastructure-led policy learning framework
Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers
Developing and testing automated driving models in the real world might be
challenging and even dangerous, while simulation can help with this, especially
for challenging maneuvers. Deep reinforcement learning (DRL) has the potential
to tackle complex decision-making and controlling tasks through learning and
interacting with the environment, thus it is suitable for developing automated
driving while not being explored in detail yet. This study carried out a
comprehensive study by implementing, evaluating, and comparing the two DRL
algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO),
for training automated driving on the highway-env simulation platform.
Effective and customized reward functions were developed and the implemented
algorithms were evaluated in terms of onlane accuracy (how well the car drives
on the road within the lane), efficiency (how fast the car drives), safety (how
likely the car is to crash into obstacles), and comfort (how much the car makes
jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based
models with modified reward functions delivered the best performance in most
cases. Furthermore, to train a uniform driving model that can tackle various
driving maneuvers besides the specific ones, this study expanded the
highway-env and developed an extra customized training environment, namely,
ComplexRoads, integrating various driving maneuvers and multiple road scenarios
together. Models trained on the designed ComplexRoads environment can adapt
well to other driving maneuvers with promising overall performance. Lastly,
several functionalities were added to the highway-env to implement this work.
The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.Comment: 6 pages, 3 figures, accepted by the 26th IEEE International
Conference on Intelligent Transportation Systems (ITSC 2023
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