Recent Advances in Pedestrian Identification Using LiDAR and Deep Learning Methods in Autonomous Vehicles
- Title
- Recent Advances in Pedestrian Identification Using LiDAR and Deep Learning Methods in Autonomous Vehicles
- Creator
- Halgatti A.; Chethana G.; Shivaprasad G.; Gayathri R.; Babu S.R.; Girish G.P.
- Description
- The myriad benefits of autonomous vehicles (AVs) encompassing passenger convenience, heightened safety, fuel consumption reduction, traffic decongestion, accident mitigation, cost-efficiency and heightened dependability have underpinned their burgeoning popularity. Prior to their full-scale integration into primary road networks substantial functional impediments in AVs necessitate resolution. An indispensable feature for AVs is pedestrian detection crucial for collision avoidance. Advent of automated driving is swiftly materializing owing to consistent deployment of deep learning (DL) methodologies for obstacle identification coupled with expeditious evolution of sensor and communication technologies exemplified by LiDAR systems. This study undertakes exploration of DL-based pedestrian detection algorithms with particular focus on YOLO and R CNN for purpose of processing intricate imagery akin to LiDAR sensor outputs. Recent epochs have witnessed DL approaches emerge as potentially potent avenue for augmenting real-time obstacle recognition and avoidance capabilities of autonomous vehicles. Within this scholarly exposition we undertake exhaustive examination of latest breakthroughs in pedestrian detection leveraging synergy of LiDAR and DL systems. This discourse comprehensively catalogues most pressing unresolved issues within realm of LiDAR-DL solutions furnishing compass for prospective researchers embarking on journey to forge forthcoming generation of economically viable autonomous vehicles. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-1168 LNNS, pp. 187-196.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Autonomous vehicles; Deep learning; LiDAR; Pedestrian detection
- Coverage
- Halgatti A., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Chethana G., RV College of Engineering, Bengaluru, India; Shivaprasad G., Faculty of Management Studies, CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Gayathri R., CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Babu S.R., Faculty of Management Studies, CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Girish G.P., Department of Finance, (a Deemed to-be-University Under Sec 3 of UGC Act 1956), ICFAI Business School, IFHE University, Hyderabad, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303173320-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Halgatti A.; Chethana G.; Shivaprasad G.; Gayathri R.; Babu S.R.; Girish G.P., “Recent Advances in Pedestrian Identification Using LiDAR and Deep Learning Methods in Autonomous Vehicles,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19033.