TORCS Dataset Papers With Code

TORCS (The Open Racing Car Simulator) is a driving simulator. It is capable of simulating the essential elements of vehicular dynamics such as mass, rotational inertia, collision, mechanics of suspensions, links and differentials, friction and aerodynamics. Physics simulation is simplified and is carried out through Euler integration of differential equations at a temporal discretization level of 0.002 seconds. The rendering pipeline is lightweight and based on OpenGL that can be turned off for faster training. TORCS offers a large variety of tracks and cars as free assets. It also provides a number of programmed robot cars with different levels of performance that can be used to benchmark the performance of human players and software driving agents. TORCS was built with the goal of developing Artificial Intelligence for vehicular control and has been used extensively by the machine learning community ever since its inception.

Martin Bauw (@BauwM) / X

UvA autonomous driving: Labbook 2020

CVPR 2018 Day 2 — notes. Day 2 of the CVPR conference was a…, by Erika Menezes

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information - Chen - 2020 - Computer-Aided Civil and Infrastructure Engineering - Wiley Online Library

A screenshot from TORCS. Download Scientific Diagram

SciREX Dataset Papers With Code

Information, Free Full-Text

Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env - Artificial Intelligence Research

Deep_Reinforcement_Learning_for_Autonomous_Driving_A_Survey

Neural Networks in Python: From Sklearn to PyTorch and Probabilistic Neural Networks