Contributions to the 10th International Cycling Safety Conference 2022 (ICSC2022)

This publication contains all contributions (extended abstracts) to the 10th International Cycling Safety Conference, which was held in Dresden, Germany, Nov. 08-10, 2022

Evaluation of event data recorders in real world crashes and full-scale crash tests

With the advent of advanced safety systems in U.S. passenger vehicles, there has been increased interest shown by automakers in recording crash related parameters that ultimately lead to the deployment of these safety systems in what are known as Event Data Recorders (EDRs). Since the only other record of these parameters, specifically crash pulse, comes from staged crash tests in a controlled environment, the advent of the EDR has become increasingly important to crash researchers. The purpose of this study is to quantify the performance of EDRs in full-scale crash tests and real world crashes. Comparison of EDRs with staged crash tests included 6 General Motors vehicles. The EDRs performed well in staged crash tests reporting delta-V accurately in five of six tests. They were able to report other crash related parameters such as driver seat belt and airbag deployment status accurately in five of six tests as well. Comparison of EDRs with real world accident reconstructions was performed for 315 General Motors cases and 10 Ford cases from the National Automotive Sampling System Crashworthiness Data System (NASS / CDS) database. Computer generated (WinSmash) values for delta-V showed the tendency to underestimate delta-V for high-speed deployment events and overestimate delta-V for low-speed nondeployment events when compared to the GM EDR. The Ford EDR showed a lack of sufficient recording duration to draw any concrete conclusions on the accuracy of its delta-V value

Use of smart technologies to collect and retain crash information

Task 1.1 of Pendant Work Package 1 has a threefold objective: firstly to develop methods and guidelines for the reconstruction of road traffic accidents, secondly to develop a database of information about public domain crash tests, and thirdly to develop methods for determining the comparability and accuracy of reconstruction methods. As part of the third aim the Description of work (2001) states: "Specific reference will be made to the use of smart technologies to collect and retain information about the crash (‘black boxes’, ‘crash recorders’). The Task will examine current capabilities and identify the main obstacles to their wider implementation." The purpose of this report is to provide an overview of the state of the art in recording information about the crash phase, including current capabilities and main obstacles to further implementation

A Review on Current eCall Systems for Autonomous Car Accident Detection

The aim of the paper is to give an overview on the existing eCall solutions for autonomous car accident detection. The requirements and expectations for such systems, considering both technological possibilities, legal regulatory criteria and market demands are discussed. Sensors utilized in e-call systems (crash sensing, systems for positional and velocity data, and communication solutions) are overviewed in the paper. Furthermore, the existing solutions for eCall devices are compared based on their level of autonomy, technical implementation and provided services

Study and validation of data recorded in the vehicles’ EDR in order to perform a road accident’s dynamic reconstruction

Road accident reconstruction is an issue which involves multiple and differentiated subjects. A collision contours’ determination requires the investigation and the analysis of all the evidence provided from highly distinct sources and remaining from uncertain and, sometimes, chaotic scenarios. People are vastly involved in traffic accident situations, either being drivers, victims, injured or witnesses. Therefore, accident investigation is a sensitive matter which requires objectiveness, accuracy, efficiency, and effectiveness, to draw faithful and factual conclusions about the collisions’ contours. The accidents reconstruction science’s main objective is to determine and describe the involved vehicles dynamics, which is accomplished by collecting and interconnect all the available evidence extracted from the impacts’ scenarios, from the vehicles, and from the involved people. In the past, many authors developed mathematical models which describe, approximately, the vehicles’ dynamics involved in a road traffic collision. Over the years, with the technology evolution and the advances on the area, multiple solutions have been created and enhanced to provide to accident reconstructionists better and more reliable evidence, allowing them to perform crash reconstructions with higher accuracy. These solutions include numerical methods, simulation and evaluation software, and tools for evidence collection. However, the introduction of the Event Data Recorder (EDR) on the vehicles consists of a great progression concerning the availability of valid and meaningful clues which can be used as inputs for the scientific crash reconstruction, since the EDR stores data that was unavailable and was difficult to deduce from the accident’s remaining evidence, previously. On the scope of this project, a vehicle data logging device was developed and tested regarding the validation of the EDR’s recorded data. The device’s purpose is to acquire the most relevant variables for crash reconstruction, which are also stored by the EDR, and provide a source of information for comparison and validation. This device was integrated with the respective sensors, programmed with a developed software, and tested on a vehicle. The tests for dynamic data acquisition consisted of travelling a defined path around the school campus, since there was not the opportunity to perform a real crash test with an EDR equipped vehicle

1990 Planning and Research Program, 1990

Planning and objectives for various departments within the Department of Transportation for 199

Model based detection and reconstruction of road traffic accidents

This thesis describes the detection and reconstruction of traffic accidents with event data recorders. The underlying idea is to describe the vehicle motion and dynamics up to the stability limit by means of linear and non-linear vehicle models. These models are used to categorize the driving behavior and to freeze the recorded data in a memory if an accident occurs. Based on these data, among others the vehicle trajectory is reconstructed with fuzzy data fusion. The side slip angle which is a crucial quantity describing the vehicle stability is estimated with non-linear state observers and Kalman-Filters. The methodologies presented may lead from accident reconstruction considered here to accident avoidance

A Deep Learning Based Model for Driving Risk Assessment

In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 drivers’ driving behavior

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria