Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review
By Jamil Fayyad, Mohammad Abdel Kareem Jaradat, Dominique Gruyer, Homayoun Najjaran. Italian National Conference on Sensors, 2020
442 citations recorded by the source corpus.
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.
Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/78c71eec4b696931b9611cd20ebab43860e81034
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Citations in the corpus
- Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review (2021, methodology): “Invariably, the overall performance of an AV system is greatly enhanced with multiple sensors of different types (smart/non-smart) and modalities (visual, infrared and radio waves) operating at different range and bandwidth (data rate) and with the data of each being incorporated to produce a fused ”
- Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, unknown): “[45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], scene understanding [68], [69], [70], tracking [71], [72],”
- Machine Learning Methods in Drug Discovery (2020, methodology): “ML models have been used in many promising technologies, such as deep learning (DL) assisted self-driving cars, advanced speech recognition, and support vector machine-based smarter search engines [1–4].”