Fluid-inspired field representation for risk assessment in road scenes
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Vol. 6, No. 4, December 2020, 401–415
Research Article
Fluid-inspired field representation for risk assessment in road scenes Xuanpeng Li1 , Lifeng Zhu1 (
), Qifan Xue1 , Dong Wang1 , and Yongjie Jessica Zhang2
c The Author(s) 2020.
Abstract Prediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from sensing technology and the dynamic environment. It leads to failure of motion planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-inspired model to estimate collision risk in road scenes. Multi-object states are detected and tracked, and then a stable fluid model is adopted to construct the risk field. Objects’ state spaces are used as the boundary conditions in the simulation of advection and diffusion processes. We have evaluated our approach on the public KITTI dataset; our model can provide predictions in the cases of misdetection and tracking error caused by occlusion. It proves a promising approach for collision risk assessment in road scenes. Keywords fluid-inspired risk field; multi-object tracking; road scenes
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Introduction
Collision risk assessment is an essential task for vehicles driving on the road. It requires predicting the likely evolution of the current traffic situation, and assessing how dangerous the future situation might be. Risk can be intuitively understood as the likelihood and severity of the damage that a vehicle of interest may suffer in the future. Some quantitative risk indicators like time-to-collision, time-to-brake, 1 School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China. E-mail: X. Li, li [email protected]; L. Zhu, [email protected] ( ); Q. Xue, xue [email protected]; D. Wang, kingeast16@ seu.edu.cn. 2 Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. E-mail: [email protected]. Manuscript received: 2020-06-01; accepted: 2020-07-21 401
and time-to-steer are widely used in advanced driver assistance systems like automatic emergency braking. But they are of limited use in complex traffic scenes: for example, they under-estimate collision risk at intersections with stopped cars, and over-estimate collision risk on a curved road [1]. Recently, risk assessment has mainly been based on prediction of future motion and estimation of collision occurrence from various sensors like cameras, LiDAR, and radar. The collision risk can be computed by integrating over all possible future trajectories and estimating collisions between each possible pair [2, 3]. Trajectory prediction plays an important role in risk assessment; approaches to modeling can be categorized into three types with an increasing degree of abstraction: physics-based, maneuverbased, and interaction-aware. Compared to the others, interaction-aware motion models can provide a reliable estimate of long-term motion and risk, but their computational requirements limit their applicability in real-time risk assessment. Another way to present the surrounding environme
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