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

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Emre Esenturk ◽  
Albert Wallace ◽  
Siddartha Khastgir ◽  
Paul Jennings
Keyword(s):  
Cluster Analysis ◽  
Autonomous Vehicles ◽  
Traffic Accident ◽  
Scenario Development ◽  
Test Scenario

Analysis Traffic Accident Prone Areas Using Cluster Analysis in Bogor City Based on WebGIS

2021 ◽  
pp. 55-62
Author(s):  
Lulu Lutfi Latifah
Keyword(s):  
Cluster Analysis ◽  
Information System ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Cluster Algorithm ◽  
Geographical Information ◽  
Prone Area ◽  
Accident Data ◽  
The City ◽  
City Police

Traffic accidents are a problem that occurs in various regions in Indonesia, especially in the city of Bogor. Based on traffic accident data obtained from the Laka Unit, the Bogor City Police experienced fluctuating movements. The use of accident data is also not optimal. This makes it difficult to see areas that have a level of vulnerability. To solve this problem, in this study an analysis was made to determine the areas prone to traffic accidents by utilizing the Geographical Information System to map the distribution of locations. The method used to analyze the accident area is using the K-Means Cluster Algorithm. The results of the research conducted showed that the highest level of vulnerability from 2014 to 2019 was in the sub-district of Tanah Sareal on Jalan K.H. Sholeh Iskandar. Several incidents of laka occurred on curves, bypasses, and in and out of vehicles. The result of this research is in a beautiful traffic accident prone area in the form of WebGIS.

scholarly journals An Investigation of Autonomous Vehicle Roundabout Situation

2019 ◽  
Vol 48 (3) ◽  
pp. 236-241
Author(s):  
Hang Cao ◽  
Máté Zöldy
Keyword(s):  
Fuel Consumption ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Test ◽  
Driving Behavior ◽  
Physical Parameters ◽  
Test Scenario ◽  
Vehicle Type ◽  
Fuel Consumption And Emissions ◽  
The Impact

The aim of this paper is to evaluate the impact of connected autonomous behavior in real vehicles on vehicle fuel consumption and emission reductions. Authors provide a preliminary theoretical summary to assess the driving conditions of autonomous vehicles in roundabout, which attempts exploring the impact of driving behavior patterns on fuel consumption and emissions, and including other key factors of autonomous vehicles to reduce fuel consumption and emissions. After summarizing, driving behavior, effective in-vehicle systems, both roundabout physical parameters and vehicle type are all play an important role in energy using. ZalaZONE’s roundabout is selected for preliminary test scenario establishment, which lays a design foundation for further in-depth testing.

scholarly journals Traffic Accident, System Model and Cluster Analysis in GIS

2015 ◽  
Vol 4 (1) ◽  
pp. 64-79
Author(s):  
Veronika Vlčková ◽  
Pavel Hrubeš
Keyword(s):  
Cluster Analysis ◽  
Traffic Accident ◽  
System Model ◽  
And Cluster Analysis

scholarly journals Clustering Analysis of Traffic Accident in Semarang City

2018 ◽  
Vol 73 ◽  
pp. 12001 ◽  
Author(s):  
Wiwik Budiawan ◽  
Bambang Purwanggono
Keyword(s):  
Cluster Analysis ◽  
Law Enforcement ◽  
Clustering Analysis ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Global Issues ◽  
High Degree ◽  
The City

Traffic accidents are one of the global issues that require serious handling. Accidents occur in different places with different incidents, which makes it difficult to determine which areas have a high degree of traffic accidents. Information about areas prone to accidents is needed by the community and law enforcement. Such information can be taken into consideration for the supervision and anticipation action especially for the police. In this study made a cluster to analyze the areas prone to accidents in the city of Semarang. The method used is cluster analysis where the grouping to determine the vulnerability of an area. The result of the research stated that the level of traffic accident vulnerability is mostly happened in Semarang - Semarang regency passing through Semarang regency. In addition, the level of vulnerability in the city of Semarang occurred on weekdays. From the validation results that have been made, the suitability of the hazardous modeling area that has been formed is: Occurs more likely on weekdays (Monday, Thursday, Friday and Sunday); At an average Kilometer of 19.75-Direction B; During Afternoon and Evening; Small and Large Vehicle Types; Cloudy, Drizzle and Rain.

scholarly journals A Study on the Evaluation Method of Autonomous Vehicle for Fixed Targets

2021 ◽  
Author(s):  
Bong-Ju Kim ◽  
Seon-Bong Lee
Keyword(s):  
Error Rate ◽  
Automobile Industry ◽  
Autonomous Vehicles ◽  
Evaluation Method ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Theoretical Formula ◽  
Test Scenario ◽  
Fixed Target ◽  
Avoidance Test

Abstract Recently, the automobile industry aims to commercialize autonomous vehicles, standardization and research and development are actively underway based on the specifications of automotive electronic control systems and the verification of functional errors. Based on this, autonomous driving technology is becoming more advanced, and it is preparing for an era of full autonomous driving through V2X technology convergence. It is also, expanding the models of ADAS applied vehicles such as FCW, BSD, LCA. Based on this, research is also actively underway to secure safety of vehicles and pedestrians, such as V2X, Localization, Fail Safe, to supplement the limitations of sensor-based autonomous driving. In this regard, this study proposes a theoretical formula for longitudinal relative distance computation for autonomous vehicles evaluation method, and uses test device such as DGPS to collects and verify data. In addition, a scenario was proposed for the fixed target, based on this, four types of test were formed to conduct the actual test, and the relative distances were compared, analyzed and verified. Comparative analysis results, in the second test of the first test scenario, the avoidance test of the fixed target in driving own lane, the minimum error rate was 0.5%, and in the second test of fourth test scenario, the avoidance test of the fixed target in driving own lane, the maximum error rate was 7.4%. The main cause of error is, it was judged as an error due to sensor recognition, depending on the scenario progress method, the condition of the test path, and the weather such as sunlight. In the future, we plan to conduct an evaluation on moving target.

scholarly journals Potential of Highly Automated Vehicles for Monitoring Fatigued Drivers and Explaining Traffic Accidents on Motorway Sections

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hyun-ho Chang ◽  
Byoung-jo Yoon
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Explanatory Power ◽  
Real Life ◽  
Statistical Correlation ◽  
Trajectory Data ◽  
Road Section ◽  
Accident Data ◽  
Vehicle Trajectory

The near-future deployment of high-level automation vehicles (AVs) can render promising opportunities to solve ongoing hindrances in modern safety-related research. Monitoring fatigued drivers on any road section is one of these challenges. Vehicle trajectory big data, monitored through AVs, include key information with which to monitor fatigued drivers on roads. To mine this upcoming opportunity, a new data-driven approach which allows the direct monitoring of fatigued drivers on road segments is proposed here for the first time. A feasible study was conducted using big vehicle trajectory data and real-life traffic accident data. The results showed that fatigued drivers on a target road section can be successfully surveyed using the driving durations from departure locations to the target road section. It was found that, with a statistical correlation of 0.90, an index for fatigued drivers has strong explanatory power about the traffic accident rate. This finding indicates that the proposed method will be a promising means by which to monitor fatigued drivers at road locations in the upcoming era of autonomous vehicles. In addition, the method is immediately practicable if vehicle trajectory data are available.

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