In which areas are machine learning and deep learning used in autonomous driving?
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In which areas are machine learning and deep learning used in autonomous driving?
Samirnuri
Autonomous cars are very closely associated with Industrial IoT. IoT combined with other technologies such as machine learning, artificial intelligence, local computing etc are providing the essential technologies for autonomous cars. Very inquisitive questions for many is how are these autonomous cars functioning. What actually is working inside to make them work without drivers taking control of the wheel. Very well known that these days cars are equipped with a lot of sensors, actuators, and controllers. These end devices are driven by software sitting on various function-specific software running on ECUs ( Electronic Control Units). Machine learning software is also part of this set.
EVO
Autonomous driving system is decision-making systems that process streams of observations coming from different on-board sensors, such as internal/external cameras, radars, LiDARs, Ultrasonic, GPS units and/or inertial sensors. These observations are used by the car’s computer to make driving decisions. There are several components in ADAS:
1. Scene recognition
2. Path Planning
3. Behavior Arbitration, or low-level path planning
4. Motion Controllers
The following deep learning technologies can be used:
1. Convolutional Neural Networks (CNN) can be used for processing spatial information, such as images, and can be viewed as image features extractors and universal non-linear function approximators.
2. Due to Recurrent Neural Networks (RNN) contains a time dependent feedback loop in its memory cell, it are especially good in processing temporal sequence data, such as text, or video streams.
3. Deep Reinforcement Learning (DRL) concept can be used in autonomous driving task such as optimal driving policy for navigating from start point to the end point and avoid collision.
Bablu123
The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices