Name
Abstract | ||
|---|---|---|
Sensing and presenting on-road information of moving vehicles is essential for fully and semi-automated driving. It is challenging to track vehicles from affordable on-board cameras in crowded scenes. The mismatch or missing data are unavoidable and it is ineffective to directly present uncertain cues to support the decision-making. In this paper, we propose a physical model based on incompressible fluid dynamics to represent the vehicle’s motion, which provides hints of possible collision as a continuous scalar riskmap. We estimate the position and velocity of other vehicles from a monocular on-board camera located in front of the ego-vehicle. The noisy trajectories are then modeled as the boundary conditions in the simulation of advection and diffusion process. We then interactively display the animating distribution of substances, and show that the continuous scalar riskmap well matches the perception of vehicles even in presence of the tracking failures. We test our method on real-world scenes and discuss about its application for driving assistance and autonomous vehicle in the future. |
Year | Venue | Field |
|---|---|---|
2018 | Intelligent Vehicles Symposium | Boundary value problem,Computer vision,Radar tracker,Computer science,Scalar (physics),Collision,Artificial intelligence,Advection,Missing data,Monocular,Trajectory |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lifeng Zhu | 1 | 110 | 10.80 |
| Xuanpeng Li | 2 | 15 | 4.48 |
| Wenjie Lu | 3 | 23 | 1.03 |
| Yongjie Zhang | 4 | 293 | 34.45 |