Driver-centric Risk Object Identification
- URL: http://arxiv.org/abs/2106.13201v1
- Date: Thu, 24 Jun 2021 17:27:32 GMT
- Title: Driver-centric Risk Object Identification
- Authors: Chengxi Li, Stanley H. Chan, Yi-Ting Chen
- Abstract summary: We propose a driver-centric definition of risk, i.e., risky objects influence driver behavior.
We formulate the task as a cause-effect problem and present a novel two-stage risk object identification framework.
A driver-centric Risk Object Identification dataset is curated to evaluate the proposed system.
- Score: 25.85690304998681
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A massive number of traffic fatalities are due to driver errors. To reduce
fatalities, developing intelligent driving systems assisting drivers to
identify potential risks is in urgent need. Risky situations are generally
defined based on collision prediction in existing research. However, collisions
are only one type of risk in traffic scenarios. We believe a more generic
definition is required. In this work, we propose a novel driver-centric
definition of risk, i.e., risky objects influence driver behavior. Based on
this definition, a new task called risk object identification is introduced. We
formulate the task as a cause-effect problem and present a novel two-stage risk
object identification framework, taking inspiration from models of situation
awareness and causal inference. A driver-centric Risk Object Identification
(ROI) dataset is curated to evaluate the proposed system. We demonstrate
state-of-the-art risk object identification performance compared with strong
baselines on the ROI dataset. In addition, we conduct extensive ablative
studies to justify our design choices.
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