289 research outputs found
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning
Decision-making module enables autonomous vehicles to reach appropriate
maneuvers in the complex urban environments, especially the intersection
situations. This work proposes a deep reinforcement learning (DRL) based
left-turn decision-making framework at unsignalized intersection for autonomous
vehicles. The objective of the studied automated vehicle is to make an
efficient and safe left-turn maneuver at a four-way unsignalized intersection.
The exploited DRL methods include deep Q-learning (DQL) and double DQL.
Simulation results indicate that the presented decision-making strategy could
efficaciously reduce the collision rate and improve transport efficiency. This
work also reveals that the constructed left-turn control structure has a great
potential to be applied in real-time.Comment: Some simulation results should be improved
Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles
In this paper, we investigate the decision making of autonomous vehicles in
an unsignalized intersection in presence of malicious vehicles, which are
vehicles that do not respect the law by not using the proper rules of the right
of way. Each vehicle computes its control input as a Nash equilibrium of a game
determined by the priority order based on its own belief: each of non-malicious
vehicle bases its order on the law, while a malicious one considers itself as
having priority. To illustrate our method, we provide numerical simulations,
with different scenarios given by different cases of malicious vehicles.Comment: IEEE Conference on Intelligent Transportation Systems (ITSC), 201
Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts
In this paper, we propose a decision making algorithm for autonomous vehicle
control at a roundabout intersection. The algorithm is based on a
game-theoretic model representing the interactions between the ego vehicle and
an opponent vehicle, and adapts to an online estimated driver type of the
opponent vehicle. Simulation results are reported.Comment: 2018 IEEE Conference on Decision and Control (CDC
Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections
Motivated by the need to develop simulation tools for verification and
validation of autonomous driving systems operating in traffic consisting of
both autonomous and human-driven vehicles, we propose a framework for modeling
vehicle interactions at uncontrolled intersections. The proposed interaction
modeling approach is based on game theory with multiple concurrent
leader-follower pairs, and accounts for common traffic rules. We parameterize
the intersection layouts and geometries to model uncontrolled intersections
with various configurations, and apply the proposed approach to model the
interactive behavior of vehicles at these intersections. Based on simulation
results in various traffic scenarios, we show that the model exhibits
reasonable behavior expected in traffic, including the capability of
reproducing scenarios extracted from real-world traffic data and reasonable
performance in resolving traffic conflicts. The model is further validated
based on the level-of-service traffic quality rating system and demonstrates
manageable computational complexity compared to traditional multi-player
game-theoretic models.Comment: 18 pages, 13 figures, 1 tabl
A Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing
Cooperative Intelligent Transportation Systems (C-ITS) will change the modes
of road safety and traffic management, especially at intersections without
traffic lights, namely unsignalized intersections. Existing researches focus on
vehicle control within a small area around an unsignalized intersection. In
this paper, we expand the control domain to a large area with multiple
intersections. In particular, we propose a Multi-intersection Vehicular
Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large
area with multiple unsignalized intersections. Firstly, a vehicular
end-edge-cloud computing framework is proposed to facilitate end-edge-cloud
vertical cooperation and horizontal cooperation among vehicles. Then, the
vehicular cooperative control problems in the cloud and edge layers are
formulated as Markov Decision Process (MDP) and solved by two-stage
reinforcement learning. Furthermore, to deal with high-density traffic, vehicle
selection methods are proposed to reduce the state space and accelerate
algorithm convergence without performance degradation. A multi-intersection
simulation platform is developed to evaluate the proposed scheme. Simulation
results show that the proposed MiVeCC can improve travel efficiency at multiple
intersections by up to 4.59 times without collision compared with existing
methods
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
Designing reliable decision strategies for autonomous urban driving is
challenging. Reinforcement learning (RL) has been used to automatically derive
suitable behavior in uncertain environments, but it does not provide any
guarantee on the performance of the resulting policy. We propose a generic
approach to enforce probabilistic guarantees on an RL agent. An exploration
strategy is derived prior to training that constrains the agent to choose among
actions that satisfy a desired probabilistic specification expressed with
linear temporal logic (LTL). Reducing the search space to policies satisfying
the LTL formula helps training and simplifies reward design. This paper
outlines a case study of an intersection scenario involving multiple traffic
participants. The resulting policy outperforms a rule-based heuristic approach
in terms of efficiency while exhibiting strong guarantees on safety
Coordinated Formation Control for Intelligent and Connected Vehicles in Multiple Traffic Scenarios
In this paper, a unified multi-vehicle formation control framework for
Intelligent and Connected Vehicles (ICVs) that can apply to multiple traffic
scenarios is proposed. In the one-dimensional scenario, different formation
geometries are analyzed and the interlaced structure is mathematically
modelized to improve driving safety while making full use of the lane capacity.
The assignment problem for vehicles and target positions is solved using
Hungarian Algorithm to improve the flexibility of the method in multiple
scenarios. In the two-dimensional scenario, an improved virtual platoon method
is proposed to transfer the complex two-dimensional passing problem to the
one-dimensional formation control problem based on the idea of rotation
projection. Besides, the vehicle regrouping method is proposed to connect the
two scenarios. Simulation results prove that the proposed multi-vehicle
formation control framework can apply to multiple typical scenarios and have
better performance than existing methods
Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection
We propose a safe DRL approach for autonomous vehicle (AV) navigation through
crowds of pedestrians while making a left turn at an unsignalized intersection.
Our method uses two long-short term memory (LSTM) models that are trained to
generate the perceived state of the environment and the future trajectories of
pedestrians given noisy observations of their movement. A future collision
prediction algorithm based on the future trajectories of the ego vehicle and
pedestrians is used to mask unsafe actions if the system predicts a collision.
The performance of our approach is evaluated in two experiments using the
high-fidelity CARLA simulation environment. The first experiment tests the
performance of our method at intersections that are similar to the training
intersection and the second experiment tests our method at intersections with a
different topology. For both experiments, our methods do not result in a
collision with a pedestrian while still navigating the intersection at a
reasonable speed.Comment: 11 pages, 6 figures, 5 Tables. arXiv admin note: text overlap with
arXiv:2105.0015
Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections
Unsignalized intersections are typically considered as one of the most
representative and challenging scenarios for self-driving vehicles. To tackle
autonomous driving problems in such scenarios, this paper proposes a curriculum
proximal policy optimization (CPPO) framework with stage-decaying clipping. By
adjusting the clipping parameter during different stages of training through
proximal policy optimization (PPO), the vehicle can first rapidly search for an
approximate optimal policy or its neighborhood with a large parameter, and then
converges to the optimal policy with a small one. Particularly, the stage-based
curriculum learning technology is incorporated into the proposed framework to
improve the generalization performance and further accelerate the training
process. Moreover, the reward function is specially designed in view of
different curriculum settings. A series of comparative experiments are
conducted in intersection-crossing scenarios with bi-lane carriageways to
verify the effectiveness of the proposed CPPO method. The results show that the
proposed approach demonstrates better adaptiveness to different dynamic and
complex environments, as well as faster training speed over baseline methods.Comment: 7 pages, 4 figure
Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic
together with human-driven vehicles. Their planning and control systems need
extensive testing, including early-stage testing in simulations where the
interactions among autonomous/human-driven vehicles are represented. Motivated
by the need for such simulation tools, we propose a game-theoretic approach to
modeling vehicle interactions, in particular, for urban traffic environments
with unsignalized intersections. We develop traffic models with heterogeneous
(in terms of their driving styles) and interactive vehicles based on our
proposed approach, and use them for virtual testing, evaluation, and
calibration of AV control systems. For illustration, we consider two AV control
approaches, analyze their characteristics and performance based on the
simulation results with our developed traffic models, and optimize the
parameters of one of them.Comment: IEEE Intelligent Transportation Systems Transaction
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