Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World

• URL: http://arxiv.org/abs/2009.12213v1
• Date: Fri, 25 Sep 2020 13:05:28 GMT
• Title: Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World
• Authors: Qi Dai, Xunnong Xu, Wen Guo, Suzhou Huang, Dimitar Filev
• Abstract summary: We propose a multi-agent based computational framework for modeling decision-making and strategic interaction at micro level for smart vehicles. Our aim is to make the framework conceptually sound and practical for a range of realistic applications, including micro path planning for autonomous vehicles.
• Score: 8.899670429041453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Markov game and best response dynamics are heavily leveraged. Our aim is to make the framework conceptually sound and computationally practical for a range of realistic applications, including micro path planning for autonomous vehicles. To this end, we first convert the would-be stochastic game problem into a closely related deterministic one by introducing risk premium in the utility function for each individual agent. We show how the sub-game perfect Nash equilibrium of the simplified deterministic game can be solved by an algorithm based on best response dynamics. In order to better model human driving behaviors with bounded rationality, we seek to further simplify the solution concept by replacing the Nash equilibrium condition with a heuristic and adaptive optimization with finite look-ahead anticipation. In addition, the algorithm corresponding to the new solution concept drastically improves the computational efficiency. To demonstrate how our approach can be applied to realistic traffic settings, we conduct a simulation experiment: to derive merging and yielding behaviors on a double-lane highway with an unexpected barrier. Despite assumption differences involved in the two solution concepts, the derived numerical solutions show that the endogenized driving behaviors are very similar. We also briefly comment on how the proposed framework can be further extended in a number of directions in our forthcoming work, such as behavioral calibration using real traffic video data, computational mechanism design for traffic policy optimization, and so on.

Related papers

• Parameterized Decision-making with Multi-modal Perception for Autonomous Driving [12.21578713219778]
  We propose a parameterized decision-making framework with multi-modal perception based on deep reinforcement learning, called AUTO. A hybrid reward function takes into account aspects of safety, traffic efficiency, passenger comfort, and impact to guide the framework to generate optimal actions.
  arXiv  Detail & Related papers  (2023-12-19T08:27:02Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network [7.812717451846781]
  We propose a behavioral model that encodes drivers' interacting intentions into latent social-psychological parameters. We develop a receding-horizon optimization-based controller for autonomous vehicle decision-making. We conduct extensive evaluations of the proposed decision-making module, in forced highway merging scenarios.
  arXiv  Detail & Related papers  (2023-10-31T03:31:09Z)
• Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout [0.0]
  Connected and autonomous vehicles have the potential to minimize energy consumption. Existing deterministic and methods created to solve the eco-driving problem generally suffer from high computational and memory requirements. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network.
  arXiv  Detail & Related papers  (2023-10-16T23:13:51Z)
• MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying [110.83590008788745]
  Motion prediction is crucial for autonomous driving systems to understand complex driving scenarios and make informed decisions. In this paper, we propose Motion TRansformer (MTR) frameworks to address these challenges. The initial MTR framework utilizes a transformer encoder-decoder structure with learnable intention queries. We introduce an advanced MTR++ framework, extending the capability of MTR to simultaneously predict multimodal motion for multiple agents.
  arXiv  Detail & Related papers  (2023-06-30T16:23:04Z)
• Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data [5.926030548326619]
  We show how the model can be inverted to estimate driver preferences from naturalistic traffic data. One distinct advantage of our approach is the drastically reduced computational burden.
  arXiv  Detail & Related papers  (2021-05-05T01:20:03Z)
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance [110.63037190641414]
  We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework. By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
  arXiv  Detail & Related papers  (2021-03-26T02:42:33Z)
• Deep Structured Reactive Planning [94.92994828905984]
  We propose a novel data-driven, reactive planning objective for self-driving vehicles. We show that our model outperforms a non-reactive variant in successfully completing highly complex maneuvers.
  arXiv  Detail & Related papers  (2021-01-18T01:43:36Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)
• A Probabilistic Framework for Imitating Human Race Driver Behavior [31.524303667746643]
  We propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules. A global target trajectory distribution is learned with Probabilistic Movement Primitives, clothoids are utilized for local path generation, and the corresponding choice of actions is performed by a neural network. Experiments in a simulated car racing setting show considerable advantages in imitation accuracy and robustness compared to other imitation learning algorithms.
  arXiv  Detail & Related papers  (2020-01-22T20:06:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.