Version 1
: Received: 13 August 2019 / Approved: 14 August 2019 / Online: 14 August 2019 (09:26:07 CEST)
How to cite:
Li, X.; Zhu, L. Fluid-based field representation for collision risk assessment on the road scenes. Preprints2019, 2019080161. https://doi.org/10.20944/preprints201908.0161.v1
Li, X.; Zhu, L. Fluid-based field representation for collision risk assessment on the road scenes. Preprints 2019, 2019080161. https://doi.org/10.20944/preprints201908.0161.v1
Li, X.; Zhu, L. Fluid-based field representation for collision risk assessment on the road scenes. Preprints2019, 2019080161. https://doi.org/10.20944/preprints201908.0161.v1
APA Style
Li, X., & Zhu, L. (2019). Fluid-based field representation for collision risk assessment on the road scenes. Preprints. https://doi.org/10.20944/preprints201908.0161.v1
Chicago/Turabian Style
Li, X. and Lifeng Zhu. 2019 "Fluid-based field representation for collision risk assessment on the road scenes" Preprints. https://doi.org/10.20944/preprints201908.0161.v1
Abstract
Prediction of the likely evolution in the traffic scenes is a challenging task because of high uncertainty of sensing technology and dynamic environment. It leads to failure of planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-based physical model to present the influence of surrounding object's motion on driving safety. In our pipeline, the input sensor could be LiDAR, camera, or multi-modal data. We use a Kalman filter to estimate the state space of each detected object, and adopt the properties of stable fluid to build a riskmap based on the density field. The noisy state space are then modeled as the boundary conditions in the simulation of advection and diffusion process. We test our approach on the public KITTI dataset and find this model could handle the short-term prediction in case of misdetection and tracking failure caused by object occlusion, which shows promising in collision risk assessment on road scenes.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
