Autonomous vehicle perception: The technology of today and tomorrow
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SLAM based methods have the potential to replace a priori techniques if their performances can be increased further.
“SLAM based methods have the potential to replace a priori techniques if their performances can be increased further [20].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [20]
Localization systems consist of external sensors such as camera systems (vision), LiDAR, RADAR, ultrasonic sensor, and global navigation satellite systems, which are significantly affected by weather conditions and geographic variances.
“Localization systems consist of external sensors such as camera systems (vision), LiDAR, RADAR, ultrasonic sensor, and global navigation satellite systems, which are significantly affected by weather conditions and geographic variances [44].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [44]
RGB-D sensors have advantages including low computational cost, long range, and an internal mechanism that limits the impact of bad weather.
“Among the advantages of these types of sensors are their low computational cost, their long-range, their ability to have an internal mechanism to limit the impact of bad weather, etc. [149].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [149]
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A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
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A Survey of Deep Learning Applications to Autonomous Vehicle Control
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- Authors
- Van Brummelen, J., O’Brien, M., Gruyer, D., & Najjaran, H.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Najjaran, Homayoun
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 89
- Pages
- 384-406
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2792919579
- Cited by
- 869
- References
- 145
- Citations by year
- 2026: 50 · 2025: 118 · 2024: 147 · 2023: 151 · 2022: 146 · 2021: 116 · 2020: 80 · 2019: 51 · 2018: 10
- FWCI
- 34.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- HAL (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (341887)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- autonomous vehicles, vehicle perception, automotive sensors, sensor fusion, localization, mapping, future research directions
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