Test Scenarios Development and Data Collection Methods for the Evaluation of Vehicle Road Departure Prevention Systems

Shen, Dan; Yan, Qiang; Li, Lingxi; Tian, Renran; Chien, Stanley; Chen, Yaobin; Sherony, Rini

doi:10.1109/tiv.2019.2919430

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Driver evasive actions before the crash have been determined by combining trajectory data with EDR data . Test designs for LDW and LDP systems have been recommended utilizing detailed trajectory information (Shen et al 2019). ROR crash trajectory and impact speed information, similar to the NCHRP 17-43 database, has been used to make the recommendations for the testing requirements for roadside safety barriers (2009;Mak et al 1986;Ross et al 1993).…”

Section: Discussionmentioning

confidence: 99%

Expansion of NASS/CDS for characterizing run-off-road crashes

Riexinger

Gabler

2020

Traffic Injury Prevention

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Objective: Runoff road (ROR) crashes account for one-third of all annual crash fatalities in the US. The National Automotive Sampling System Crashworthiness Data System (NASS/CDS) is a dataset which may be used to understand the nature of ROR crashes. Despite the wealth of coded data available in NASS/CDS, this dataset lacks coded information about the roadside environment and the off-road trajectory of the vehicle. This information would be useful for determining lane departure warning (LDW) benefits, residual safety problems, performance of current safety hardware, lane marking inventory, LDW test procedure development, radius of curvature characterization, and effectiveness of ESC. The purpose of this paper is to demonstrate a methodology for expanding the data available in NASS/CDS to form and validate a specialized road departure database. Methods: Observed, measured, and reconstructed data elements were extracted from NASS/CDS and compiled into the National Cooperative Highway Research Program (NCHRP) 17-43 database. Observed variables were primarily coded from the scene photographs and included information such as the lane markings, and geometry of the roadside cross-section. Additional variables were measured from the scaled scene diagrams including the path of the vehicle, road dimensions, and roadside object positions. The vehicle impact speed and departure speed were reconstructed using the WinSMASH delta-v, roadside object characteristics, and vehicle path. Two studies were conducted to demonstrate the usefulness of the NCHRP 17-43 database in evaluating both vehicle-based and infrastructure-based ROR countermeasures. Results: The resulting NCHRP 17-43 database includes 1,581 NASS/CDS cases representing 510,154 ROR crashes. Analysis of the database found that drivers which crashed following an overcorrection were younger than drivers which did not overcorrect. This may indicate that inexperienced drivers are more likely to overcorrect when departing the roadway. The 85 th percentile impact severity of ROR crashes, which occur on roads with a speed limit greater than 65 mph, is higher than the practical worst-case test conditions for roadside barriers. Conclusions: The NCHRP 17-43 database contains information extracted from NASS/CDS cases to better understand the nature of ROR crashes, driver behavior in these crashes, and the potential benefits of both vehicle-based and infrastructure-based ROR countermeasures.

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Section: Discussionmentioning

confidence: 99%

Expansion of NASS/CDS for characterizing run-off-road crashes

Riexinger

Gabler

2020

Traffic Injury Prevention

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“…The road data acquisition was assessed using seven variables on straight and curved roads. The results indicated that the sensing system performs accurately while recording the accident data and they are displayed effectively on the vehicle systems [36]. The road data was collected using a floating device that is placed on the road.…”

Section: Literature Reviewmentioning

confidence: 99%

Road Accident Data Collection Systems in Developing and Developed Countries: A Review

Rabbani¹,

Musarat²,

Alaloul³

et al. 2022

IJIE

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Over time, the rate of road accidents is increasing day by day. Eventhough with various advancements, the developing countries are still struggling to eliminate this issue. One such possible solution could be adopting the advanced automated tools for the road accident data collection systems, based on which the policymakers can adopt the necessary strategies to avoid the accidents. In this manner, a review has been conducted where a comparison of road accident data collection systems was made between developing and the developed countries. It was observed that to establish a standardized system for road accident prevention, records from prior accidents play a key role in the evaluation and prediction of the accident, damage, and consequences. Moreover, the manual and digital approaches of data collection were highlighted, which revealed that digital approaches are reliable and time saving for data gathering and its compilation. This comparison is beneficial in terms of future developments in developing countries and the developed countries can act as a role model and their strategies can be implemented to reduce the accidents.

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“…In their proposed work they have studied that backing crashes are increasing in day-to-day life in vehicles due to which they have used IOT sensors like camera, and a controller to detect the obstacles and height of it to prevent accidents, they use two approaches to solve the problem, firstly they use free space boundary (Algorithm) detection for obstacles and secondly, they use 3D point spread (Algorithm) to measure height of the obstacles in the wide field-of view [5].…”

Section: ░ 3 Lietrature Reviewmentioning

confidence: 99%

Detecting Vehicles at Hair Pin Curves using Internet of Things (IOT)

Goel

Kushwaha

Srivastava

et al. 2022

IJEER

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With increase in the number of vehicles especially personal vehicles there is an increase in accidents due to which, every year almost 1.30 million people die due to accidents involving vehicles. There is no effective way to prevent accidents and know the location where the accidents happen to get help easily, especially in hilly areas. In Hilly areas, there are no straight roads for vehicles and sometimes we encounter so many curves, some of which are dangerous that we have no idea if there are any other vehicles coming or not if not maneuvered properly can cause an accident. Our proposed system uses a microcontroller named raspberry pi and different IOT sensors like ultrasonic sensors and devices like LED and LCD which is an effective, and cost- effective solution to prevent the accidents at hair pin curves.

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Test Scenarios Development and Data Collection Methods for the Evaluation of Vehicle Road Departure Prevention Systems

Cited by 16 publications

References 15 publications

Expansion of NASS/CDS for characterizing run-off-road crashes

Expansion of NASS/CDS for characterizing run-off-road crashes

Road Accident Data Collection Systems in Developing and Developed Countries: A Review

Detecting Vehicles at Hair Pin Curves using Internet of Things (IOT)

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