Deep learning-based image recognition for autonomous driving

Deep Reinforcement Learning framework for Autonomous Driving

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework fo...

Multi-Modal Multi-Task Deep Learning for Autonomous Driving

Several deep learning approaches have been applied to the autonomous driving task, many employing end-toend deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This pap...

Object Detection for Autonomous Driving Using Deep Learning

Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving

With the development of state-of-art deep reinforcement learning, we can efficiently tackle continuous control problems. But the deep reinforcement learning method for continuous control is based on historical data, which would make unpredicted decisions in unfamiliar scenarios. Combining deep reinforcement learning and safety based control can get good performance for self-driving and collisio...

Autonomous Driving through Intelligent Image Processing and Machine Learning

Overview. This abstract describes the current research in the area of autonomously driving of a vehicle along different road courses [1]. The focus of this paper are two main aspects: firstly, parameters of the environment are being extracted from a video image coming from one single camera which is installed in or in front of the vehicle which is to drive along the road course; secondly, the i...