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.

Performance after training across all environments for at most 2.5

UAVVaste Dataset Papers With Code

Virtual to Real Reinforcement Learning for Autonomous Driving

LayoutBench Dataset

Martin Bauw (@BauwM) / X

Learning a Driving Simulator – arXiv Vanity

PDF) Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving

Using Keras and Deep Deterministic Policy Gradient to play TORCS

PDF] Distributed Approach for implementation of A3C on TORCS

Graphical TORCS driver model and the corresponding training configurations.

Imitation Learning

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

Anomaly Detection by ADGM / LVAE