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.

Deep_Reinforcement_Learning_for_Autonomous_Driving_A_Survey

PDF) The WCCI 2008 simulated car racing competition

BURST Dataset Papers With Code

Sensor Fusion: Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data

Using Keras and Deep Deterministic Policy Gradient to play TORCS

PDF) Securing Future Autonomous & Connected Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

Performance after training across all environments for at most 2.5

attention_and_driving/self-driving.md at 2.0 · ykotseruba/attention_and_driving · GitHub

PDF) Speed traffic sign detection on the CARLA simulator using YOLO

Martin Bauw (@BauwM) / X

Newsletters Papers With Code

Martin Bauw (@BauwM) / X

TORCS screen-shot of DRL based lane keeping

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