quantifying-safety-of-learning-based-self-driving-control-using-almost-barrier-functions
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- Host: GitHub
- Owner: ZhizhenQin
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README.md
Quantifying-Safety-of-Learning-based-Self-Driving-Control-Using-Almost-Barrier-Functions
This repository contains the parameters of Kinematic and Dynamic models used in the paper (revised) Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions.
Vehicle Dynamics Models
Kinematic Model | Dynamic Model
:-------------------------:|:------------------------:
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The figures above illustrate the kinematic and dynamic vehicle models. The only parameter for the Kinematic model is the wheelbase of the vehicle, which we use $L=2.9$ meters.
For Dynamic model, the parameters are shown in the table below:
| Parameter | Description | Value | |-----------|-----------------------------------------------------------------------------|-------| | $L$ | Wheelbase (m) | 4.35 | | $m$ | Mass (kg) | 1600 | | $Iz$ | Yaw inertia (kgm^2) | 7500 | | $lf$ | Center of gravity to the front axle (m) | 2.175 | | $lr$ | Center of gravity to the rear axle (m) | 2.175 | | $cf$ | Coefficient for front tire cornering stiffness linear approximation (N/rad) | 2e4 | | $c_r$ | Coefficient for rear tire cornering stiffness linear approximation (N/rad) | 2e4 |
The implementations of the dynamics are based on [1], [2] and [3]. We are working to get the full codebase of the barrier function training and verification soon.
[1] Snider JM. Automatic Steering Methods for Autonomous Automobile Path Tracking. 2009.
[2] Sakai A, Ingram D, Dinius J, Chawla K, Raffin A, Paques A. PythonRobotics: a Python code collection of robotics algorithms. Published online 2018. doi: https://doi.org/10.48550/arXiv.1808.10703
[3] Dong C. PathTrackingBicycle. GitHub repository. https://github.com/Derekabc/PathTrackingBicycle. 2020.
Owner
- Name: Zhizhen Qin
- Login: ZhizhenQin
- Kind: user
- Repositories: 1
- Profile: https://github.com/ZhizhenQin