Race Driver Evaluation at a Driving Simulator using a physical Model and
a Machine Learning Approach
- URL: http://arxiv.org/abs/2201.12939v1
- Date: Thu, 27 Jan 2022 07:32:32 GMT
- Title: Race Driver Evaluation at a Driving Simulator using a physical Model and
a Machine Learning Approach
- Authors: Julian von Schleinitz, Thomas Schwarzhuber, Lukas W\"orle, Michael
Graf, Arno Eichberger, Wolfgang Trutschnig and Andreas Schr\"oder
- Abstract summary: We present a method to study and evaluate race drivers on a driver-in-the-loop simulator.
An overall performance score, a vehicle-trajectory score and a handling score are introduced to evaluate drivers.
We show that the neural network is accurate and robust with a root-mean-square error between 2-5% and can replace the optimisation based method.
- Score: 1.9395755884693817
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Professional race drivers are still superior to automated systems at
controlling a vehicle at its dynamic limit. Gaining insight into race drivers'
vehicle handling process might lead to further development in the areas of
automated driving systems. We present a method to study and evaluate race
drivers on a driver-in-the-loop simulator by analysing tire grip potential
exploitation. Given initial data from a simulator run, two optimiser based on
physical models maximise the horizontal vehicle acceleration or the tire
forces, respectively. An overall performance score, a vehicle-trajectory score
and a handling score are introduced to evaluate drivers. Our method is thereby
completely track independent and can be used from one single corner up to a
large data set. We apply the proposed method to a motorsport data set
containing over 1200 laps from seven professional race drivers and two amateur
drivers whose lap times are 10-20% slower. The difference to the professional
drivers comes mainly from their inferior handling skills and not their choice
of driving line. A downside of the presented method for certain applications is
an extensive computation time. Therefore, we propose a Long-short-term memory
(LSTM) neural network to estimate the driver evaluation scores. We show that
the neural network is accurate and robust with a root-mean-square error between
2-5% and can replace the optimisation based method. The time for processing the
data set considered in this work is reduced from 68 hours to 12 seconds, making
the neural network suitable for real-time application.
Related papers
- FastRLAP: A System for Learning High-Speed Driving via Deep RL and
Autonomous Practicing [71.76084256567599]
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL)
Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations.
The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
arXiv Detail & Related papers (2023-04-19T17:33:47Z) - Autonomous Slalom Maneuver Based on Expert Drivers' Behavior Using
Convolutional Neural Network [0.39146761527401425]
This paper presents a method to mimic drivers' behavior using a convolutional neural network (CNN)
CNN model computes the desired steering wheel angle and sends it to an adaptive PD controller.
In some trials, the presented method performed a smoother maneuver compared to the expert drivers.
arXiv Detail & Related papers (2023-01-18T10:47:43Z) - 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) - Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle.
In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
arXiv Detail & Related papers (2022-07-05T16:33:20Z) - An Adaptive Human Driver Model for Realistic Race Car Simulations [25.67586167621258]
We provide a better understanding of race driver behavior and introduce an adaptive human race driver model based on imitation learning.
We show that our framework can create realistic driving line distributions on unseen race tracks with almost human-like performance.
arXiv Detail & Related papers (2022-03-03T18:39:50Z) - Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car.
We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
arXiv Detail & Related papers (2021-09-16T13:10:27Z) - Driver2vec: Driver Identification from Automotive Data [44.84876493736275]
Driver2vec is able to accurately identify the driver from a short 10-second interval of sensor data.
Driver2vec is trained on a dataset of 51 drivers provided by Nervtech.
arXiv Detail & Related papers (2021-02-10T03:09:13Z) - Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap
Time Simulation Using Machine Learning [0.0]
This paper describes a machine learning approach to generating an accurate prediction of the racing line in real-time on desktop processing hardware.
The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver.
arXiv Detail & Related papers (2021-02-03T22:34:22Z) - Testing the Safety of Self-driving Vehicles by Simulating Perception and
Prediction [88.0416857308144]
We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps.
We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
arXiv Detail & Related papers (2020-08-13T17:20:02Z) - DeepRacing: Parameterized Trajectories for Autonomous Racing [0.0]
We consider the challenging problem of high speed autonomous racing in a realistic Formula One environment.
DeepRacing is a novel end-to-end framework, and a virtual testbed for training and evaluating algorithms for autonomous racing.
This virtual testbed is released under an open-source license both as a standalone C++ API and as a binding to the popular Robot Operating System 2 (ROS2) framework.
arXiv Detail & Related papers (2020-05-06T21:35:48Z)
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.