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.

CUHK03 Benchmark (Person Re-Identification)

Adversarial Reinforcement Learning for Steering Cars from Virtual to Real World

A Structured Evidence-Based Literature Review on Discharge - ARCHI

Machine Learning Datasets

PDF) Perspective, Survey and Trends: Public Driving Datasets and Toolsets for Autonomous Driving Virtual Test

cTDaR Dataset Papers With Code

Using Keras and Deep Deterministic Policy Gradient to play TORCS

Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis

PDF) Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles