Abstract
Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.
Highlights
Autonomous vehicles (AVs) have made impressive technological progress in recent years; these noticeable advancements have brought the concept of self-driving cars into reality
Layer four concludes the perception layer, where different objects can be identified by their behavior or specific properties and trajectories to build a proper representation of their interactions, which are inputs to higher-level processing, such as decision making at the fifth level
The field of autonomous vehicles and self-driving cars is vast, as it involves a great variety of subjects ranging from electronics, sensors, and hardware to control and decision-making algorithms, as well as all the social and economic aspects
Summary
Autonomous vehicles (AVs) have made impressive technological progress in recent years; these noticeable advancements have brought the concept of self-driving cars into reality. Deep learning have been utilized in intelligent (AI) and learning known as deep learning hasalgorithms gained huge popularity in several different aspects systems, such as perception, mapping, and decision making These algorithms applications relatedoftoAV object detection, object identification, road situation recognition, and more have proven ability to solve of these difficulties, including computational loadsaspects faced byof AV generally roboticstheir issues [11]. This review paper will focus on two components of combinations the AV systems: perception, and localization provides an overview of the advantages of recent sensor and their applications in AVs and mapping. As well as different sensor fusion algorithms utilized in the Section it evaluates describesvarious the task of applicationsperception of deep learning algorithms compares with the traditional environmental and provides anand overview ofthem the latest deep learningalgorithms.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
