A reinforcement learning framework to enhance the ramp merging capabilities of autonomous vehicles
While many automotive companies are now invested in the development of self-driving cars, the vehicles created so far have not yet attained the safety levels necessary for them to be deployed on a large-scale. For this to happen, the vehicles will need to be able to tackle a wide variety of challenges on the road both safely and effectively.
How can mobile robots perceive and understand the environment correctly, even if parts of the environment are occluded by other objects? This is a key question that must be solved for self-driving vehicles to safely navigate in large crowded cities. While humans can imagine complete physical structures of objects even…
In nature, flying animals sense coming changes in their surroundings, including the onset of sudden turbulence, and quickly adjust to stay safe. Engineers who design aircraft would like to give their vehicles the same ability to predict incoming disturbances and respond appropriately.
Researchers at Oxford University's Department of Computer Science, in collaboration with colleagues from Bogazici University, Turkey, have developed a novel artificial intelligence (AI) system to enable autonomous vehicles (AVs) achieve safer and more reliable navigation capability, especially under adverse weather conditions and GPS-denied driving scenarios. The results have been published…