Identification of Test Scenarios for Autonomous Vehicles Using Fatal Accident Data

Author(s):  
Mert Aydin ◽  
Mustafa Ilhan Akbas
2018 ◽  
Vol 10 (3) ◽  
pp. 345-352 ◽  
Author(s):  
Ryan M. McManus ◽  
Abraham M. Rutchick

With the imminent advent of autonomous vehicles (AVs) comes a moral dilemma: How do people assign responsibility in the event of a fatal accident? AVs necessarily create conditions in which “drivers” yield agency to a machine. The current study examines how people make attributions of blame and praise in this context. Varying the features of AV technology affected how responsible a “driver” (who purchased the vehicle) is perceived to be following a deadly crash. The findings provide support for agency and commission as crucial bases of moral judgment. They also raise questions about how morally contradictory actions are perceived and underscore the need for research examining how moral responsibility is distributed among multiple potentially culpable agents. Pragmatically, these findings suggest that regulating (or declining to regulate) how AVs are programmed may strongly influence perceptions of moral and legal culpability.


2020 ◽  
Vol 15 (2) ◽  
pp. 31-48
Author(s):  
Vilma Jasiūnienė ◽  
Rasa Vaiškūnaitė

Network-wide road safety assessment throughout the whole network is one of the four road infrastructure safety management procedures regulated by Directive 2019/1936/EC of the European Parliament and of the Council of 23 October 2019 Аmending Directive 2008/96/EC on Road Infrastructure Safety Management and one of the methods for determining the direction of investment in road safety. So far, the implementation of the procedure has been lightly regulated and adapted using various road safety indicators. The paper describes the evaluation of road accident data that is one of the criteria for conducting a network-wide road safety assessment. Taking into consideration that networkwide road safety assessment is a proactive road safety activity, the paper proposes to conduct road safety assessment considering the expected fatal accident density. Such assessment makes it possible to assess the severity of accidents, and the use of the predicted road accident data on calculating the introduced road accident rate contributing to the prevention of accidents. The paper describes both the empirical Bayes method for predicting road accidents and the application of one of the road safety indicators – the expected fatal accident density – to determine five road safety categories across the road network. The paper demonstrates the application of the proposals submitted to Lithuanian highways using road accident and traffic data for the period 2014–2018.


2018 ◽  
Vol 50 (2) ◽  
pp. 113
Author(s):  
Wan Muhammad Taufiq Wan Hussin ◽  
Tarmiji Masron ◽  
Mohd Norarshad Nordin

This study aims to analyze fatal accident rate involving all vehicle types in the North East District of Penang. It covers fatal accident data within the duration of three years from 2011 till 2013. The primary objective is to analyze the spatial pattern and fatal accident black spot areas using Geographic Information System (GIS) application. Average Nearest Neighbor (ANN) tool is used to analyze fatal accident spatial pattern, while Kernel Density Estimation (KDE) method is utilized for fatal accident analysis. The Fatal Accident rates in 2011, 2012 and 2013 were the highest with each accounted up to 90, 88 and 91 cases. The result of ANN shows that the fatal accident pattern for 2011, 2012 and 2013 is clustered with null hypothesis rejected. The KDE analysis result shows that most fatal accident black spot areas happened at main road areas or segments.


2021 ◽  
Vol 26 (5) ◽  
pp. 699-712
Author(s):  
Yimin Su ◽  
Lin Wang

AbstractAutonomous vehicles must pass effective standard tests to verify their reliability and safety. Accordingly, it is very important to establish a complete scientific test and evaluation system for autonomous vehicles. A comprehensive framework incorporating the design of test scenarios, selection of evaluation indexes, and establishment of an evaluation system is proposed in this paper. The aims of the system are to obtain an objective and quantitative score regarding the intelligence of autonomous vehicles, and to form an automated process in the future development. The proposed framework is built on a simulation platform to ensure the feasibility of the design and implementation of the test scenarios. The design principle for the test scenarios is also presented. To reduce subjective influences, the proposed framework selects objective indexes from four aspects: safety, comfort, driving performance, and standard regulations. The order relation analysis method is adopted to formulate the index weights, and fuzzy comprehensive evaluation is used to quantify the scores. Finally, a numerical example is provided to visually demonstrate the evaluation results for the autonomous vehicles scored by the proposed framework.


Author(s):  
Kenneth R. Agent ◽  
Jerry G. Pigman ◽  
Joel M. Weber

The objectives were to examine current criteria and procedures used for setting speed limits and to determine appropriate speed limits for various types of roads. The study involved a review of literature, collection and analysis of speed data, and collection and analysis of accident data. The speed data included moving speed data on various highway types and a comparison of speed data before and after speed limit changes. Accident data were collected at locations where speed limits were changed and also on sections of adjacent Interstates with different speed limits. The speed data indicate that a large percentage of vehicle speeds exceed posted speed limits, with the highest percentage being on urban Interstates and two-lane parkways. The speeds for trucks were slightly lower than for cars. A comparison of speed data at locations where speed limits were changed showed only slight differences. A comparison of accident rates at adjacent sections of Interstate where the speed limit was 88.6 km/hr (55 mph) and 104.7 km/hr (65 mph) did not find a substantial difference in the total, injury, or fatal accident rates. Except where legislatively mandated speed limits apply, the 85th-percentile speed should be used to establish speed limits. Maximum limits are given for various types of roadways. Different speed limits for cars and trucks are recommended for some roadways. An engineering study must be conducted before the speed limit should be changed for any specific section of roadway.


2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hyun-ho Chang ◽  
Byoung-jo Yoon

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.


Author(s):  
Rupak Majumdar ◽  
Aman Mathur ◽  
Marcus Pirron ◽  
Laura Stegner ◽  
Damien Zufferey

AbstractSystematic testing of autonomous vehicles operating in complex real-world scenarios is a difficult and expensive problem. We present Paracosm, a framework for writing systematic test scenarios for autonomous driving simulations. Paracosm allows users to programmatically describe complex driving situations with specific features, e.g., road layouts and environmental conditions, as well as reactive temporal behaviors of other cars and pedestrians. A systematic exploration of the state space, both for visual features and for reactive interactions with the environment is made possible. We define a notion of test coverage for parameter configurations based on combinatorial testing and low dispersion sequences. Using fuzzing on parameter configurations, our automatic test generator can maximize coverage of various behaviors and find problematic cases. Through empirical evaluations, we demonstrate the capabilities of Paracosm in programmatically modeling parameterized test environments, and in finding problematic scenarios.


Sign in / Sign up

Export Citation Format

Share Document