Researchers develop novel deep learning-based detection system for autonomous vehicles. ScienceDaily . Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2023 / 11 / 231130113233.htm ...

These systems depend heavily on data annotation to train AI/ML models that enable real-time perception and decision-making. Raw data is collected from a suite of sensors, including LiDAR, radar, ...

Used as a learning experience to apply knowledge about reinforcement learning. Quite a bit is boilerplate code to allow Tensorflow to interface with the Carla environment. Relevant functions that ...

Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving ...

Traffic Sign Detection using Deep Learning Techniques in Autonomous Vehicles Abstract: Autonomous vehicle is an emerging topic for both researchers and the automobile industry as companies are still ...

TechCrunch Disrupt 2025 hits Moscone West in San Francisco from October 27–29, bringing together more than 10,000 startup and ...

This project is about training a vehicle, such as a car or a drone, to navigate in 3D space (either the real world, or a realistic virutal environment) autonomously based on the principle of Deep ...

Traditional approaches to autonomous vehicles (AVs) rely on using millions of miles of driving data in conjunction with even more miles of simulated data as inputs to supervised machine learning ...

Elon Musk says Tesla vehicles could soon be fully self-driving, with no human intervention needed. But self-driving cars rely on deep learning, and the technology just isn’t there yet.