Development and evaluation of potential accident scenarios involving pedestrians and AEB-equipped vehicles to demonstrate the efficiency of an enhanced open-source simulation framework

Schachner, Martin; Sinz, Wolfgang; Thomson, Robert; Klug, Corina

doi:10.1016/j.aap.2020.105831

Cited by 19 publications

(15 citation statements)

References 16 publications

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“…Compared to this, the average speed of trams in Vienna is approximately 15 km/h, according to official data (Wiener Wiener Linien, 2020) which might be due to the idle times at stops. A detailed analysis of the surrounding traffic and the influence due to emergency braking (Schachner et al, 2020) would be of additional value. The speeds represented in the current paper should be understand as worst-case prediction.…”

Section: Discussionmentioning

confidence: 99%

Tram to Pedestrian Collisions—Priorities and Potentials

Lackner

Heinzl

Rizzi

et al. 2022

Front. Future Transp.

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To improve mobility in cities in line with environmental goals, in urban traffic, trams represent an increasingly important means of transport. Due to the close interaction with other road users, this makes collisions with trams fairly frequent. This study has investigated accidents between trams and vulnerable road users resulting in personal injury, aimed at identifying priorities for simulating collisions between trams and pedestrians to assess passive safety measures. Tram accident data collection established throughout Europe from multiple sources and with varying degree of details, have been combined and analysed. These analyses comprise risk assessments per km-driven and general tram accident partner and site type evaluations, with more detailed analyses on accident site distance to the closest tram stop and injured body regions, respectively. In total, 7,535 tram-pedestrian accident resulting in 8,802 pedestrian injuries, collected in the year 2000–2021, was analysed. Accident risk ranges from 0.934 accidents per number of tram (million) km-driven, for slight injuries to 0.063 for fatal injuries. Pedestrians represent a large proportion of tram accident collision partners, especially for severe and fatal accidents. In accidents between trams and pedestrians, 3% of reported injuries are fatal, 23% severe and 74% minor. Generally, low-speed accidents close to tram stops often leading to minor injuries were observed to be of significant importance (<20m to the GPS location of a stop). Analysis of accidents was done bases on gender of the pedestrian showing overall similar involvements in accident with slight difference for various age groups and sites. Regardless of injury severity, the most frequently injured body region in accidents involving a tram is the head. Likewise, injuries sustained to the thorax, especially for higher injury severities are of high relevance, followed by injuries to the lower extremities. Based on this study, recommendations for developing reasonable tram-pedestrian accident scenarios for virtual testing can be derived for further optimisation of pedestrian safety of trams.

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Section: Discussionmentioning

confidence: 99%

Tram to Pedestrian Collisions—Priorities and Potentials

Lackner

Heinzl

Rizzi

et al. 2022

Front. Future Transp.

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“…In this case, the pedestrian follows a given trajectory. Schachner et al (2020) propose a different approach generating a scenario catalog of critical car-pedestrian conflict situations by varying the following parameters: vehicle speed, pedestrian speed, and pedestrian waiting time. The trajectory for vehicle and pedestrian was previously defined.…”

Section: Scenario Generationmentioning

confidence: 99%

“…Table1), one based on P.E.A.R.S. and the other based on the setting applied inSchachner et al (2020).…”

mentioning

confidence: 99%

Influence of different pedestrian behavior models on the performance assessment of autonomous emergency braking (AEB) systems via virtual simulation

Oliveira

Meywerk

Schories

et al. 2022

Proceedings of the 7th International Digital Human Modeling Symposium -

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Pedestrian safety is a central topic in the automotive industry because of the high number of deaths in carto-pedestrian accidents. Different systems have been developed to protect pedestrians and other vulnerable road users. So-called Active Safety Systems are used to avoid possible collisions with the VRU or to mitigate injury severity by reducing the collision speed in case the collision can't longer be prevented. The autonomous emergency braking system (AEB) is one of these systems and aims to intervene in conflict situations by stopping the car, Haus et al. (2019). The performance assessment of the AEB System can be done via virtual simulation. One crucial aspect is the modeling of pedestrian behavior. Current studies use a simple pedestrian behavior model, sometimes called a trajectory-based model, in which the pedestrian moves with constant speed on a given path and without any interaction with the environment. This study investigates how the AEB Performance in virtual environments is influenced by using a more realistic pedestrian behavior model based on reinforcement learning approach, a particular Machine Learning branch perfectly suited for modeling decision-making processes. For that, a generic AEB-System, the trajectorybased pedestrian model, and the reinforcement learning model were implemented in CARLA Simulator. A scenario catalog was created by varying some parameters and used to evaluate the front collisions with and without the AEB system. The study indicates that due to some pedestrian reactions of the reinforcement learning model, like unexpected stopping in front of the car, the performance of the AEB-System is reduced.

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“…The implementation of new safety features in vehicles is believed to be due to vehicle demands and market strategy. Some highlighted benefits of AEB-equipped vehicles include their ability to automatically detect potentially dangerous scenarios, such as pedestrians (Schachner et al, 2020). Moreover, AEB systems can mitigate collision impact by decreasing the vehicle's velocity in case avoidance is not possible (Haus et al, 2019).…”

Section: Summary Of Previous Studies On Aebmentioning

confidence: 99%

Consumers' Perception of Automatic Emergency Braking (AEB): Theoretical Model and Construct Development

Ishanuddin¹,

Sukadarin

Nawi³

et al. 2021

JSAEM

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Due to remarkable developments, various automated driving systems, known as Advanced Driver Assistance Systems (ADAS), have been implemented, and the entire range of human driver activity in modern vehicles is undergoing changes. However, whether or not the consumers will accept and satisfy the emerging technologies, especially Automatic Emergency Braking (AEB), remains a big question. Therefore, it is vital to understand how vehicle consumers or potential consumers decide to use or not to use, and what they expect or perceive of AEB. This work intends to review the published works on AEB to understand the current market of the technology. In order to investigate the user's perception, a developed theoretical model is planned to be adopted. The review was conducted by searching from Google Scholar and the ScienceDirect database through Universiti Malaysia Pahang online databases. The knowledge and understanding of the current AEB market and theoretical model will help researchers plan the next steps to explore consumer or potential consumer perception.

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Development and evaluation of potential accident scenarios involving pedestrians and AEB-equipped vehicles to demonstrate the efficiency of an enhanced open-source simulation framework

Cited by 19 publications

References 16 publications

Tram to Pedestrian Collisions—Priorities and Potentials

Tram to Pedestrian Collisions—Priorities and Potentials

Influence of different pedestrian behavior models on the performance assessment of autonomous emergency braking (AEB) systems via virtual simulation

Consumers' Perception of Automatic Emergency Braking (AEB): Theoretical Model and Construct Development

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