A Quantitative Method to Determine What Collisions Are Reasonably Foreseeable and Preventable

The development of Automated Driving Systems (ADSs) has made significant progress in the last years. To enable the deployment of Automated Vehicles (AVs) equipped with such ADSs, regulations concerning the approval of these systems need to be established. In 2021, the World Forum for Harmonization of Vehicle Regulations has approved a new United Nations regulation concerning the approval of Automated Lane Keeping Systems (ALKSs). An important aspect of this regulation is that "the activated system shall not cause any collisions that are reasonably foreseeable and preventable." The phrasing of "reasonably foreseeable and preventable" might be subjected to different interpretations and, therefore, this might result in disagreements among AV developers and the authorities that are requested to approve AVs. The objective of this work is to propose a method for quantifying what is "reasonably foreseeable and preventable". The proposed method considers the Operational Design Domain (ODD) of the system and can be applied to any ODD. Having a quantitative method for determining what is reasonably foreseeable and preventable provides developers, authorities, and the users of ADSs a better understanding of the residual risks to be expected when deploying these systems in real traffic. Using our proposed method, we can estimate what collisions are reasonably foreseeable and preventable. This will help in setting requirements regarding the safety of ADSs and can lead to stronger justification for design decisions and test coverage for developing ADSs.Comment: 25 pages, 9 figures, 2 table

PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation

Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk. As a result, they are only applicable in scenarios where those assumptions hold. To address this issue, we present a novel data-driven Probabilistic RISk Measure derivAtion (PRISMA) method. The PRISMA method is used to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). Because we adopt a data-driven approach to predict the possible future evolutions of traffic participant trajectories, less assumptions on these trajectories are needed. Since the PRISMA is not bound to specific assumptions, multiple SSMs for different types of scenarios can be derived. To calculate the probability of the specific event, the PRISMA method uses Monte Carlo simulations to estimate the occurrence probability of the specified event. We further introduce a statistical method that requires fewer simulations to estimate this probability. Combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. It is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.Comment: 26 pages, 4 figures, 1 tabl

D5.1 MeBeSafe - Trial Design

The report describe the research design for the project MeBeSafe, where different nudges as well as coaching measures to behave more safely in traffic are tested

Overview of main accident scenarios in car-tocyclist accidents for use in AEB-system test protocol

The overall number of fatalities in road traffic accidents in Europe is decreasing. Unfortunately, the number of fatalities among cyclists does not follow this trend with the same rate [5]. In the Netherlands, a major share of killed cyclists in traffic accidents was the result of a collision with a motorisedvehicle [2]. The automotive industry is making a significant effort in the development and implementation of safety systems in cars to avoid or mitigate an imminent crash with vulnerable road users, and more specifically with cyclists. The current state‐of‐the‐art of active safety systems, AutonomousEmergency Braking (AEB), is being widely introduced. A car equipped with AEB makes use of onboard sensors such as camera and radar, to track and trace traffic participants that possibly interfere with the trajectory of the car. This information is used to warn the driver in case of a possibly criticalsituation and/or to brake in case the driver does not respond and the risk of collision does not decrease. Currently, AEB systems that are designed to avoid car‐to‐car collisions are part of the Euro NCAP star rating. In 2016, Euro NCAP will include AEB systems for pedestrians in the star rating. It isthe intention of Euro NCAP to include AEB systems for cyclists in the star rating beginning of 2018 [3]. To support and prepare the introduction of Cyclist‐AEB systems and the resulting consumer tests of such systems, TNO has taken the initiative to set‐up a consortium of car manufacturers and supplierswith the support of Euro NCAP laboratories (such as BASt) to develop a testing system and test protocol for Cyclist‐AEB systems. This paper reports the first steps towards this protocol in which an indepth road accident study is performed to determine what accident scenarios are most relevant for car‐to‐cyclist collisions. Data of killed and seriously injured cyclists due to collision with a passenger car were included in this study. An overview is given for the following European countries: Germany, the Netherlands, Sweden, France, Italy, and the United Kingdom

Describing I2V Communication in Scenarios for Simulation-Based Safety Assessment of Truck Platooning

V2X communication plays an important role in the transition towards connected, cooperative, automated driving. Wireless communication enables instant information exchange between vehicles (V2V) to support, e.g., platooning, and between the infrastructure and vehicles (I2V) to inform vehicles on, e.g., the local speed limit information or the approach of an accident location. In the Horizon 2020 HEADSTART project, we have shown how to test V2V communication in a scenario-based safety assessment framework. Safety assessment aims to determine the impact on safety in the case of potentially critical scenarios, e.g., due to, or in parallel to deterioration of communication. In this study, we extend this methodology with the incorporation of I2V communication. The developed method allows us to treat V2V and I2V communication independently. We demonstrate the method in the use case of an Intelligent Speed Adaptation I2V-functionality for platooning trucks. The practical implementation of test descriptions that consider the potential deterioration of communication signals in the standardized OpenSCENARIO format is shown. The study illustrates how tests are performed in a hardware-in-the-loop setup specifically developed for testing platooning functions. The availability of a test method that is capable of dealing with V2X communication is an important step towards the implementation of type approval methods for Cooperative, Connected and Automated Mobility (CCAM) systems