Autonomous Drifting with 3 Minutes of Data via Learned Tire Models

• URL: http://arxiv.org/abs/2306.06330v2
• Date: Mon, 16 Oct 2023 22:05:45 GMT
• Title: Autonomous Drifting with 3 Minutes of Data via Learned Tire Models
• Authors: Franck Djeumou and Jonathan Y.M. Goh and Ufuk Topcu and Avinash Balachandran
• Abstract summary: We propose a novel family of tire force models based on neural ordinary differential equations and a neural-ExpTanh parameterization. Experiments with a customized Toyota Supra show that scarce amounts of driving data is sufficient to achieve high-performance autonomous drifting.
• Score: 19.549514141225863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially in emergency situations where high forces are required. To this end, we propose a novel family of tire force models based on neural ordinary differential equations and a neural-ExpTanh parameterization. These models are designed to satisfy physically insightful assumptions while also having sufficient fidelity to capture higher-order effects directly from vehicle state measurements. They are used as drop-in replacements for an analytical brush tire model in an existing nonlinear model predictive control framework. Experiments with a customized Toyota Supra show that scarce amounts of driving data -- less than three minutes -- is sufficient to achieve high-performance autonomous drifting on various trajectories with speeds up to 45mph. Comparisons with the benchmark model show a $4 \times$ improvement in tracking performance, smoother control inputs, and faster and more consistent computation time.

Related papers

• First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling [38.468291768795865]
  In unstable systems, online adaptation may not be fast enough to ensure reliable simultaneous learning and control. We present a Bayesian meta-learning MPC framework to enable rapid online adaptation. Experiments on a Toyota Supra show that the framework enables reliable control in dynamic drifting maneuvers.
  arXiv  Detail & Related papers  (2024-10-31T18:02:30Z)
• Neural Network Tire Force Modeling for Automated Drifting [0.2999888908665658]
  We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. We deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory.
  arXiv  Detail & Related papers  (2024-07-18T17:58:01Z)
• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential [71.53284767149685]
  We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
  arXiv  Detail & Related papers  (2023-12-22T16:29:55Z)
• 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)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments [34.80538284957094]
  This paper introduces a lightweight framework for resource-efficient driver activity recognition. The framework enhances 3D MobileNet, a neural architecture optimized for speed in video classification. It achieves a threefold reduction in model size and a 1.4-fold improvement in inference time.
  arXiv  Detail & Related papers  (2023-11-10T10:07:07Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• Formulation and validation of a car-following model based on deep reinforcement learning [0.0]
  We propose and validate a novel car following model based on deep reinforcement learning. Our model is trained to maximize externally given reward functions for the free and car-following regimes. The parameters of these reward functions resemble that of traditional models such as the Intelligent Driver Model.
  arXiv  Detail & Related papers  (2021-09-29T08:27:12Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)

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.