A real-time explainable traffic collision inference framework based on probabilistic graph theory

Liu, Xiaoming; Liu, Yu; Zhou, Yadong; Shen, Chao; Guan, Xiaohong

doi:10.1016/j.knosys.2020.106442

Cited by 6 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liuet al [10] used the real-time traffic features extracted from the Tweets to build probabilistic graphs that capture the causal relationships among the features and collision results. Next, a Bayesian network model based on those graphs is used to estimate the collision probabilities.…”

Section: Related Workmentioning

confidence: 99%

KnowGo: An Adaptive Learning-Based Multi-model Framework for Dynamic Automotive Risk Assessment

Mundt¹,

Kumara

Heuvel

et al. 2022

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

In autonomous driving systems, the level of monitoring and control expected from the vehicle and the driver change in accordance with the level of automation, creating a dynamic risk environment where risks change according to the level of automation. Moreover, the input data and their essential features for a given risk model can also be inconsistent, heterogeneous, and volatile. Therefore, risk assessment systems must adapt to changes in the automation level and input data content to ensure that both the risk criteria and weighting reflect the actual system state, which can change at any time. This paper introduces KnowGo, a learning-based dynamic risk assessment framework that provides a risk prediction architecture that can be dynamically reconfigured in terms of risk criterion, risk model selection, and weighting in response to dynamic changes in the operational environment. We validated the KnowGo framework with five types of risk scoring models implemented using data-driven and rule-based methods.

show abstract

Section: Related Workmentioning

confidence: 99%