2000 character limit reached
Dynamic Risk Assessment Methodology with an LDM-based System for Parking Scenarios
Published 5 Apr 2024 in cs.CV, cs.SY, and eess.SY | (2404.04040v1)
Abstract: This paper describes the methodology for building a dynamic risk assessment for ADAS (Advanced Driving Assistance Systems) algorithms in parking scenarios, fusing exterior and interior perception for a better understanding of the scene and a more comprehensive risk estimation. This includes the definition of a dynamic risk methodology that depends on the situation from inside and outside the vehicle, the creation of a multi-sensor dataset of risk assessment for ADAS benchmarking purposes, and a Local Dynamic Map (LDM) that fuses data from the exterior and interior of the car to build an LDM-based Dynamic Risk Assessment System (DRAS).
- B. Nie, Q. Li, S. Gan, B. Xing, Y. Huang, and S.E. Li., “Safety envelope of pedestrians upon motor vehicle conflicts identified via active avoidance behaviour,” Scientific Reports, vol. 11, p. 3996, 2021.
- S. Kolekar, J. de Winter, and D. Abbink, “Human-like driving behaviour emerges from a risk-based driver model,” Nature Communications, vol. 11, p. 4850, 2020.
- SGI, “Danger zones” [Online]. Available: https://sgi.sk.ca/pro-driver/-/knowledge_base/pro-drivers/danger-zones. [Accessed: Apr. 28, 2023].
- S. Dafrallah, A. Amine, S. Mousset, and A. Bensrhair, “Monocular pedestrian orientation recognition based on capsule network for a novel collision warning system” IEEE Access,2021.
- International Organization for Standardization, “ISO 22840:2010 ITS—Devices to aid reverse manoeuvres—Extended-range backing aid systems (ERBA)” [Online]. Available: https://www.iso.org/standard/36521.html.
- International Organization for Standardization, “ISO 17386:2004 Transport information and control systems — Manoeuvring Aids for Low Speed Operation (MALSO) — Performance requirements and test procedures,” [Online]. Available: https://www.iso.org/standard/30634.html.
- Bosch Mobility Solutions, “Ultrasonic sensor,” [Online]. Available: https://www.bosch-mobility.com/en/solutions/sensors/ultrasonic-sensor/. [Accessed: Apr. 28, 2023]
- J. Harpster, R. Huey, and N. Lerner, “Field Measurement of Naturalistic Backing Behavior,” in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 1996.
- P.L. Olson, “Driver Perception-Response Time,” in Human Factors in Traffic Safety (R. Dewar and P.L. Olson Eds.), Lawyers & Judges Publishing Company, 2002.
- T Urselmann , PN Cañas , JD Ortega , M Nieto., “Semi-automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application,” ECCV Workshops, 2022.
- Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl., “Center-based 3D Object Detection and Tracking,” CVPR, 2021.
- MMDetection3D Contributors. MMDetection3D: OpenMMLab next-generation platform for general 3D object detection, 2020.
- H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. Erin Liong, Q. Xu, A. Krishnan, Yu Pan, G. Baldan, O. Beijbom., “nuScenes: A multimodal dataset for autonomous driving, 2019.
- L. Andreone, R. Brignolo, S. Damiani, F. Sommariva, G. Vivo and S. Marco., “D8.1.1-SAFESPOT Final Report,” SAFESPOT Final Report–Public version(July), 2010.
- J. Eggert, D. Aguirre Salazar, T. Puphal and B. Flade.,“Driving Situation Analysis with Relational Local Dynamic Maps (R-LDM),” FAST-Zero Symposium, 2017.
- M. García, I. Urbieta, M. Nieto, J. González de Mendibil and O. Otaegui.,“iLDM: An Interoperable Graph-Based Local Dynamic Map,” Vehicles vol.4(1), pp42-59, 2022.
- M.Scholtes, L.Westhofen, L.R.Turner, K.Lotto, M.Schuldes, H.Weber, N.Wagener, C.Neurohr, M.Bollmann, F.Körtke, J.Hiller, M.Hoss and J.Bock.,“6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment.”, 2021.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.