Identification of Traffic Accident Patterns via Cluster Analysis and Test Scenario Development for Autonomous Vehicles

Abstract

Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Intersection two-vehicle crash scenario specification for automated vehicle safety evaluation using sequence analysis and Bayesian networks

“…Therefore, such a scenario library would complement scenario generating methodologies developed based on vehicle kinematic data sources such as naturalistic driving data and crash reconstruction data. Comparing with previous efforts in systematically generating scenarios for AV testing using crash data, such as the work by Nitsche et al (2017), Sander and Lubbe (Sander & Lubbe, 2018), and the Safety Pool scenario library of Warwick University (Esenturk et al, 2021(Esenturk et al, , 2022, the approach we proposed in this paper is a significant improvement. That is because 1) the crash sequence modeling captures the interactive dynamics and 2) the Bayesian network provides a comprehensive but interpretable representation of the relationships between all types of factors involved in crashes.…”

“…Prior efforts in developing test scenarios using historical crash data have developed characterization of crashes to be used as representative scenarios for the evaluation of ADAS or ADS. The crash characterization was developed by summarizing and mining patterns in crash attributes (Najm et al, 2007;Nitsche et al, 2017;Sander & Lubbe, 2018;Sui et al, 2019;Watanabe et al, 2019;Esenturk et al, 2021Esenturk et al, , 2022. The end products from prior efforts -representative scenarios -lack considerations of crash progression, dynamics, and mechanisms, which are important information to distinguish crashes and their outcomes (Song et al, 2021;Wu et al, 2016).…”

A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas

“…Clustering Approach [1] traffic load analysis improved k-means clustering algorithm [2] traffic congestion analysis self-organizing maps neural network [3] traffic state classification k-medoids algorithm [4] road network level identification k-means algorithm [5] traffic congestion analysis grey relational clustering model [6] traffic accidents and pattern extraction ROCK algorithm [7] traffic accident pattern identification COOLCAT algorithm [8] traffic accident factor analysis k-means algorithm [9] road traffic accident modeling a comparative study of machine learning classifiers [10] traffic accident black spots identification HDBSCAN algorithm [11] traffic congestion analysis k-means algorithm [12] driving behavior risk analysis k-means algorithm [13] optimal path routing a modified K-medoids algorithm [14] analysis of pedestrian crash fatalities and severe injuries KDE method [15] traffic-management system DBSCAN agorithm [16] severity of traffic accident analysis DBSCAN algorithm [17] highway safety assessment k-means algorithm [18] pedestrian crash severity analysis KDE method [19] detection of road segments of spatially prolonged and high traffic accident risk a clustering algorithm based on the Gestalt principle of proximity…”

Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems

“…Te previous work of Esenturk et al [26,27] analyzed the UK accident data collection STATS19 (https://www.data. gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/ road-safety-data) with the goal of generating valuable test scenarios for ADSs.…”