Progressive LiDAR adaptation for road detection
Open accessAbstract
Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises. However, the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection (PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-the-art road detection models and is currently top of the publicly accessible benchmark leader-board.
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Semantic segmentation in subareas such as medical image segmentation, road detection, and human parsing is useful in various AIoT applications.
“Semantic segmentation in many subareas such as medical image segmentation [75], road detection [85], and human parsing [86], are useful in various AIoT applications.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [85]
The authors in [85] leverage residual learning to fuse multilevel RGB image features and Lidar features via a residual structure in a cascaded manner.
“Besides, the authors in [85] leverage the residual learning idea to fuse the multilevel RGB image features and Lidar features via a residual structure in a cascaded manner.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [85]
Fusing multi-modal data at the feature level is most prevalent, which can be further categorized into early fusion [186], late fusion [185], and fusion at multiple levels [85], [182].
“Among them, fusing multi-modal data at the feature level is most prevalent, which can be further categorized into three groups, i.e., early fusion [186], late fusion [185], and fusion at multiple levels [85], [182].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [85]
The self-driving system should perceive the driving environment, such as detecting road, traffic-sign, pedestrian, and car, estimating the intention of cars and pedestrians and predicting their trajectories, and measure the pose and location of landmarks for SLAM.
“The self-driving system should perceive the driving environment, such as detecting road [85], traffic-sign [259], pedestrian [260], and car [261], estimating the intention of cars and pedestrians and predicting their trajectories [244], [245]. Besides, it should also measure the pose and location of landmarks (e.g., trafficsigns) for SLAM [137].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [85]
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- Authors
- Chen, Z., Zhang, J., & Tao, D.
- Affiliations
- The University of Sydney (AU)
- Hangzhou Dianzi University (CN)
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2019-05
- Volume
- 6
- Issue
- 3
- Pages
- 693-702
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W2932012013
- Cited by
- 205
- References
- 45
- Citations by year
- 2026: 9 · 2025: 35 · 2024: 30 · 2023: 32 · 2022: 34 · 2021: 36 · 2020: 23 · 2019: 6
- FWCI
- 11.30
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CAA Journal of Automatica Sinica (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- road detection, LiDAR, domain adaptation, KITTI dataset, urban scenes, illumination variation
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