136 research outputs found
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
A Game-Theoretic Approach to Decision Making for Multiple Vehicles at Roundabout
In this paper, we study the decision making of multiple autonomous vehicles
at a roundabout. The behaviours of the vehicles depend on their aggressiveness,
which indicates how much they value speed over safety. We propose a distributed
decision-making process that balances safety and speed of the vehicles. In the
proposed process, each vehicle estimates other vehicles' aggressiveness and
formulates the interactions among the vehicles as a finite sequential game.
Based on the Nash equilibrium of this game, the vehicle predicts other
vehicles' behaviours and makes decisions. We perform numerical simulations to
illustrate the effectiveness of the proposed process, both for safety (absence
of collisions), and speed (time spent within the roundabout)
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
Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours
To improve the safety and efficiency of the intelligent transportation
system, particularly in complex urban scenarios, in this paper a game theoretic
decision-making framework is designed for connected automated vehicles (CAVs)
at unsignalized roundabouts considering their personalized driving behaviours.
Within the decision-making framework, a motion prediction module is designed
and optimized using model predictive control (MPC) to enhance the effectiveness
and accuracy of the decision-making algorithm. Besides, the payoff function of
decision making is defined with the consideration of vehicle safety, ride
comfort and travel efficiency. Additionally, the constraints of the
decision-making problem are constructed. Based on the established
decision-making model, Stackelberg game and grand coalition game approaches are
adopted to address the decision making of CAVs at an unsignalized roundabout.
Three testing cases considering personalized driving behaviours are carried out
to verify the performance of the developed decision-making algorithms. The
testing results show that the proposed game theoretic decision-making framework
is able to make safe and reasonable decisions for CAVs in the complex urban
scenarios, validating its feasibility and effectiveness. Stackelberg game
approach shows its advantage in guaranteeing personalized driving objectives of
individuals, while the grand coalition game approach is advantageous regarding
the efficiency improvement of the transportation system.Comment: This paper has been accepetd by IEEE Transactions on Vehicular
Technolog
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
Due to the complex and dynamic character of intersection scenarios, the
autonomous driving strategy at intersections has been a difficult problem and a
hot point in the research of intelligent transportation systems in recent
years. This paper gives a brief summary of state-of-the-art autonomous driving
strategies at intersections. Firstly, we enumerate and analyze common types of
intersection scenarios, corresponding simulation platforms, as well as related
datasets. Secondly, by reviewing previous studies, we have summarized
characteristics of existing autonomous driving strategies and classified them
into several categories. Finally, we point out problems of the existing
autonomous driving strategies and put forward several valuable research
outlooks
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
Adaptive Robust Game-Theoretic Decision Making for Autonomous Vehicles
In a typical traffic scenario, autonomous vehicles are required to share the
road with other road participants, e.g., human driven vehicles, pedestrians,
etc. To successfully navigate the traffic, a cognitive hierarchy theory such as
level-k framework, can be used by the autonomous agents to categorize the
agents based on their depth of strategic thought and act accordingly. However,
mismatch between the vehicle dynamics and its predictions, and improper
classification of the agents can lead to undesirable behavior or collision.
Robust approaches can handle the uncertainties, however, might result in a
conservative behavior of the autonomous vehicle. This paper proposes an
adaptive robust decision making strategy for autonomous vehicles to handle
model mismatches in the prediction model while utilizing the confidence of the
driver behavior to obtain less conservative actions. The effectiveness of the
proposed approach is validated for a lane change maneuver in a highway driving
scenario
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
A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments
Emerging mobility systems, e.g., connected and automated vehicles (CAVs),
shared mobility, and electric vehicles, provide the most intriguing opportunity
for enabling users to better monitor transportation network conditions and make
better decisions for improving safety and transportation efficiency. However,
before connectivity and automation are deployed en masse, a thorough evaluation
of CAVs is required-ranging from numerical simulation to real-world public
roads. Assessment of the performance of CAVs in scaled testbeds has recently
gained momentum due to the flexibility they offer to conduct quick, repeatable
experiments that could go one step beyond simulation. This article introduces
the Information and Decision Science Lab's Scaled Smart City (IDSC), a 1:25
research and educational scaled robotic testbed that is capable of replicating
different real-world urban traffic scenarios. IDSC was designed to
investigate the effect of emerging mobility systems on safety and
transportation efficiency. On the educational front, IDSC can be used for
(a) training and educating graduate students by exposing them to a balanced mix
of theory and practice, (b) integrating the research outcomes into existing
courses, (c) involving undergraduate students in research, (d) creating
interactive educational demos, and (e) reaching out to high-school students. In
our exposition, we also present a real-time control framework that can be used
to coordinate CAVs at traffic scenarios such as crossing signal-free
intersections, merging at roadways and roundabouts, cruising in congested
traffic, passing through speed reduction zones, and lane-merging or passing
maneuvers. Finally, we provide a tutorial for applying our framework in
coordinating robotic CAVs to a multi-lane roundabout scenario and a
transportation corridor in IDSC.Comment: 35 pages, 9 figure
Interactive Decision Making for Autonomous Vehicles in Dense Traffic
Dense urban traffic environments can produce situations where accurate
prediction and dynamic models are insufficient for successful autonomous
vehicle motion planning. We investigate how an autonomous agent can safely
negotiate with other traffic participants, enabling the agent to handle
potential deadlocks. Specifically we consider merges where the gap between cars
is smaller than the size of the ego vehicle. We propose a game theoretic
framework capable of generating and responding to interactive behaviors. Our
main contribution is to show how game-tree decision making can be executed by
an autonomous vehicle, including approximations and reasoning that make the
tree-search computationally tractable. Additionally, to test our model we
develop a stochastic rule-based traffic agent capable of generating interactive
behaviors that can be used as a benchmark for simulating traffic participants
in a crowded merge setting
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