3,124 research outputs found
Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven
vehicles(HVs) will coexist on the same road. The safety and reliability of AVs
will depend on their social awareness and their ability to engage in complex
social interactions in a socially accepted manner. However, AVs are still
inefficient in terms of cooperating with HVs and struggle to understand and
adapt to human behavior, which is particularly challenging in mixed autonomy.
In a road shared by AVs and HVs, the social preferences or individual traits of
HVs are unknown to the AVs and different from AVs, which are expected to follow
a policy, HVs are particularly difficult to forecast since they do not
necessarily follow a stationary policy. To address these challenges, we frame
the mixed-autonomy problem as a multi-agent reinforcement learning (MARL)
problem and propose an approach that allows AVs to learn the decision-making of
HVs implicitly from experience, account for all vehicles' interests, and safely
adapt to other traffic situations. In contrast with existing works, we quantify
AVs' social preferences and propose a distributed reward structure that
introduces altruism into their decision-making process, allowing the altruistic
AVs to learn to establish coalitions and influence the behavior of HVs.Comment: arXiv admin note: substantial text overlap with arXiv:2202.0088
Uncertainty-Aware Online Merge Planning with Learned Driver Behavior
Safe and reliable autonomy solutions are a critical component of
next-generation intelligent transportation systems. Autonomous vehicles in such
systems must reason about complex and dynamic driving scenes in real time and
anticipate the behavior of nearby drivers. Human driving behavior is highly
nuanced and specific to individual traffic participants. For example, drivers
might display cooperative or non-cooperative behaviors in the presence of
merging vehicles. These behaviors must be estimated and incorporated in the
planning process for safe and efficient driving. In this work, we present a
framework for estimating the cooperation level of drivers on a freeway and plan
merging maneuvers with the drivers' latent behaviors explicitly modeled. The
latent parameter estimation problem is solved using a particle filter to
approximate the probability distribution over the cooperation level. A
partially observable Markov decision process (POMDP) that includes the latent
state estimate is solved online to extract a policy for a merging vehicle. We
evaluate our method in a high-fidelity automotive simulator against methods
that are agnostic to latent states or rely on assumptions
about actor behavior
Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning
Lane change in dense traffic is considered a challenging problem that
typically requires the recognition of an opportune and appropriate opportunity
for maneuvers. In this work, we propose a chance-aware lane-change strategy
with high-level model predictive control (MPC) through curriculum reinforcement
learning (CRL). The embodied MPC in our framework is parameterized with
augmented decision variables, where full-state references and regulatory
factors concerning their relative importance are introduced. Furthermore, to
improve the convergence speed and ensure a high-quality policy, effective
curriculum design is integrated into the reinforcement learning (RL) framework
with policy transfer and enhancement. Then the proposed framework is deployed
to numerical simulations towards dense and dynamic traffic. It is noteworthy
that, given a narrow chance, the proposed approach generates high-quality
lane-change maneuvers such that the vehicle merges into the traffic flow with a
high success rate of 96%
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible
trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
This is because the obstacle-free volume in spacetime is very small in these
scenarios for the vehicle to drive through. However, that does not mean the
task is infeasible since human drivers are known to be able to drive amongst
dense traffic by leveraging the cooperativeness of other drivers to open a gap.
The traditional methods fail to take into account the fact that the actions
taken by an agent affect the behaviour of other vehicles on the road. In this
work, we rely on the ability of deep reinforcement learning to implicitly model
such interactions and learn a continuous control policy over the action space
of an autonomous vehicle. The application we consider requires our agent to
negotiate and open a gap in the road in order to successfully merge or change
lanes. Our policy learns to repeatedly probe into the target road lane while
trying to find a safe spot to move in to. We compare against two
model-predictive control-based algorithms and show that our policy outperforms
them in simulation.Comment: Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA), 2020. Updated Github repository link
Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors
Understanding the intention of vehicles in the surrounding traffic is crucial
for an autonomous vehicle to successfully accomplish its driving tasks in
complex traffic scenarios such as highway forced merging. In this paper, we
consider a behavioral model that incorporates both social behaviors and
personal objectives of the interacting drivers. Leveraging this model, we
develop a receding-horizon control-based decision-making strategy, that
estimates online the other drivers' intentions using Bayesian filtering and
incorporates predictions of nearby vehicles' behaviors under uncertain
intentions. The effectiveness of the proposed decision-making strategy is
demonstrated and evaluated based on simulation studies in comparison with a
game theoretic controller and a real-world traffic dataset
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