A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
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 …
complex urban scenarios, in this paper a game theoretic decision-making framework is …
Deep reinforcement learning based game-theoretic decision-making for autonomous vehicles
This letter presents an approach for implementing game-theoretic decision-making in
combination with deep reinforcement learning to allow vehicles to make decisions at an …
combination with deep reinforcement learning to allow vehicles to make decisions at an …
Learning game-theoretic models of multiagent trajectories using implicit layers
P Geiger, CN Straehle - Proceedings of the AAAI Conference on …, 2021 - ojs.aaai.org
For prediction of interacting agents' trajectories, we propose an end-to-end trainable
architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable …
architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable …
Anytime game-theoretic planning with active reasoning about humans' latent states for human-centered robots
A human-centered robot needs to reason about the cognitive limitation and potential
irrationality of its human partner to achieve seamless interactions. This paper proposes an …
irrationality of its human partner to achieve seamless interactions. This paper proposes an …
Yield or rush? social-preference-aware driving interaction modeling using game-theoretic framework
The newly formed hybrid traffic flow where Human-driven Vehicles (HV) share roads with
Autonomous Vehicles (AV) is a foreseeable trend in the modern transportation system …
Autonomous Vehicles (AV) is a foreseeable trend in the modern transportation system …
Towards a systematic computational framework for modeling multi-agent decision-making at micro level for smart vehicles in a smart world
We propose a multi-agent based computational framework for modeling decision-making
and strategic interaction at micro level for smart vehicles in a smart world. The concepts of …
and strategic interaction at micro level for smart vehicles in a smart world. The concepts of …
Data-driven scenario specification for AV–VRU interactions at urban roundabouts
Detailed specifications of urban traffic from different perspectives and scales are crucial for
understanding and predicting traffic situations from the view of an autonomous vehicle (AV) …
understanding and predicting traffic situations from the view of an autonomous vehicle (AV) …
Bounded risk-sensitive markov games: Forward policy design and inverse reward learning with iterative reasoning and cumulative prospect theory
Classical game-theoretic approaches for multi-agent systems in both the forward policy
design problem and the inverse reward learning problem often make strong rationality …
design problem and the inverse reward learning problem often make strong rationality …