An active inference model of car following: Advantages and applications

• URL: http://arxiv.org/abs/2303.15201v1
• Date: Mon, 27 Mar 2023 13:39:26 GMT
• Title: An active inference model of car following: Advantages and applications
• Authors: Ran Wei, Anthony D. McDonald, Alfredo Garcia, Gustav Markkula, Johan Engstrom, and Matthew O'Kelly
• Abstract summary: Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability.
• Score: 6.905724739762358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets.

Related papers

• Guiding Attention in End-to-End Driving Models [49.762868784033785]
  Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. We study how to guide the attention of these models to improve their driving quality by adding a loss term during training. In contrast to previous work, our method does not require these salient semantic maps to be available during testing time.
  arXiv  Detail & Related papers  (2024-04-30T23:18:51Z)
• SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control [59.20038082523832]
  We present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving applications. We develop a novel model equipped with a subject control mechanism, which allows the generative model to leverage diverse external data sources for producing varied and useful data.
  arXiv  Detail & Related papers  (2024-03-28T14:07:13Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [51.244807332133696]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving. RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• IDM-Follower: A Model-Informed Deep Learning Method for Long-Sequence Car-Following Trajectory Prediction [24.94160059351764]
  Most car-following models are generative and only consider the inputs of the speed, position, and acceleration of the last time step. We implement a novel structure with two independent encoders and a self-attention decoder that could sequentially predict the following trajectories. Numerical experiments with multiple settings on simulation and NGSIM datasets show that the IDM-Follower can improve the prediction performance.
  arXiv  Detail & Related papers  (2022-10-20T02:24:27Z)
• A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering [6.9485501711137525]
  We propose a methodology that combines rule-based modeling with data-driven learning. Our results show that driver models based on our hybrid rule-based and data-driven approach can accurately capture real-world driving behavior.
  arXiv  Detail & Related papers  (2021-08-29T11:07:14Z)
• A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN [59.57221522897815]
  We propose a neural network model based on trajectories information for driving behavior recognition. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.
  arXiv  Detail & Related papers  (2021-03-01T06:47:29Z)
• Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing [2.40966076588569]
  We develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing. We show that our model can learn more accurately than a purely parametric model and generalize better than a purely non-parametric model.
  arXiv  Detail & Related papers  (2020-11-17T16:24:10Z)
• The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations [0.0]
  We investigate the composition of training data in vehicle trajectory prediction. We show that the models employing our semantic vector representation outperform the numerical model when trained on an adequate data set.
  arXiv  Detail & Related papers  (2020-09-30T20:00:11Z)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)
• An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset [7.151393153761375]
  This paper introduces an approach to learn a short-term memory (LSTM)-based model for imitating the behavior of a self-driving model. The experimental results show that our model outperforms several models in driving action prediction.
  arXiv  Detail & Related papers  (2020-02-14T05:28:15Z)

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.