Autonomous Driving Architectures, Perception and Data Fusion: A Review
| Autor: | John Barry, Joseph Walsh, De Jong Yeong, Gustavo Velasco-Hernandez |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject 010401 analytical chemistry 02 engineering and technology Sensor fusion 01 natural sciences Object detection Field (computer science) 020202 computer hardware & architecture 0104 chemical sciences Domain (software engineering) Software Component (UML) Perception 0202 electrical engineering electronic engineering information engineering Systems engineering Representation (mathematics) business media_common |
| Zdroj: | ICCP 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP) |
| DOI: | 10.1109/iccp51029.2020.9266268 |
| Popis: | Over the last 10 years, huge advances have been made in the areas of sensor technologies and processing platforms, pushing forward developments in the field of autonomous vehicles, mostly represented by self-driving cars. However, the complexity of these systems has been also increased in terms of the hardware and software within them, especially for the perception stage in which the goal is to create a reliable representation of the vehicle and the world. In order to manage this complexity, several architectural models have been proposed as guidelines to design, develop, operate and deploy self-driving solutions for real applications. In this work, a review on autonomous driving architectures is presented, classifying them into technical or functional architectures depending on the domain of their definition. In addition, the perception stage of self-driving solutions is analysed as a component of the architectures, detailing into the sensing part and how data fusion is used to perform localisation, mapping and object detection. Finally, the paper is concluded with additional thoughts on the actual status and future trends in the field. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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