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.

Machine Learning Datasets

Yet Another Driving Simulator OpenROUTS3D: The Driving Simulator for Teleoperated Driving

Adversarial Reinforcement Learning for Steering Cars from Virtual to Real World

SUMMIT Dataset Papers With Code

TORCS screen-shot of DRL based lane keeping

Generalization of TORCS car racing controllers with artificial neural networks and linear regression analysis - ScienceDirect

Using Keras and Deep Deterministic Policy Gradient to play TORCS

Martin Bauw (@BauwM) / X

TORCS Dataset Papers With Code

Deep_Reinforcement_Learning_for_Autonomous_Driving_A_Survey

Pseudo-code of dynamic scheduling framework with grouped whales (DSF.GW)

Visual TORCS network controller pipeline. At each time-step a raw 64×64