Time-to-collision analysis of pedestrian and pedal-cycle accidents for the development of autonomous emergency braking systems

Lenard, James; Welsh, Ruth; Danton, Russell

doi:10.1016/j.aap.2018.02.028

Cited by 38 publications

(14 citation statements)

References 4 publications

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“…Findings would need to be checked in car-to-VRU scenarios where VRU detection is more challenging compared to detection of a car which path is easier to predict and visibility generally higher. Accident reconstructions were used by Lenard et al (2018) to determine the requirement of ADAS sensors. From the analysis of pedestrian and cyclist accidents they found that the range needed to detect most of the pedestrians and cyclists is similar and in between 42 to 50m whereas the Field of View (FOV) varied significantly between cyclists and pedestrians: of ±80° for cyclists (total 160°) and only ±20° (total 40°) for pedestrians.…”

Section: Introductionmentioning

confidence: 99%

Car-to-cyclist forward collision warning effectiveness evaluation: a parametric analysis on reconstructed real accident cases

Char

Serre

Compigne

et al. 2020

International Journal of Crashworthiness

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The objective of the study is to quantify the benefits of an earlier brake activation by the drivers potentially achieved by a Forward Collision Warning (FCW) system in simulated car-to-cyclist accident scenarios. A parametric analysis is performed by varying the detection sensor Field Of View (FOV), the FCW trigger time and the driver's reaction lag time to the FCW. Almost two thousand and three hundred car-to-cyclist accidents are clustered in the following five main scenarios: crossing nearside (33%), crossing farside (22%), longitudinal (5%), turning left (12%) and turning right (22%). The remaining is clustered in Others group (6%). For all accident cases, original accident kinematics are processed through Matlab scripts from which FCW FOV, FCW trigger time and driver's reaction can be modified. The Matlab scripts return the new accident kinematics which can result in the accident being avoided or mitigated. This study shows that a 70° FOV, a FCW trigger time of 2.6s before the impact and a 0.6s driver's reaction to the FCW has a positive result in 82% of the accident cases with 78% being avoided and 4% mitigated. Concerning the parameters, the FOV has a greater influence on the avoidance rates compared to FCW trigger time and driver's reaction. Our study also reveals that FCW system has a higher benefit in the crossing farside scenario and a lower benefit in the turning right scenario. This paper highlights generic characteristics of FCW systems to optimize safety benefit for the different accident scenarios.

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Section: Introductionmentioning

confidence: 99%

Car-to-cyclist forward collision warning effectiveness evaluation: a parametric analysis on reconstructed real accident cases

Char

Serre

Compigne

et al. 2020

International Journal of Crashworthiness

View full text Add to dashboard Cite

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“…1s). Lenard et al (2018) also analyzed parameters for an AEB applied to a British sample of 175 pedestrians and 127 cyclists' cases. Their study showed that with the same FOV definition as the one in this study, an AEB system with a FOV of 80° is enough to detect 90% of cyclists and 20° for pedestrians.…”

Section: Discussionmentioning

confidence: 99%

Analysis of pre-crash characteristics of passenger car to cyclist accidents for the development of advanced drivers assistance systems

Char

Serre²

2020

Accident Analysis & Prevention

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The purpose of this study was to analyze car-to-cyclist accidents to determine the challenges for an active safety system on car to avoid accidents. Based on 2261 car-to-cyclist accidents provided by in-depth accident databases, accidents are analyzed more specifically from kinematic reconstructions. The main accident scenarios are determined: crossing nearside, crossing farside, longitudinal, turning (right and left) and others. Proportion of brakes activation by the drivers before the impact was also given for those scenarios. The relative positions of the cyclists to the vehicle are analyzed from few seconds before the impact until the crash. It is observed that one second before the impact most of the cyclists were at a lateral distance smaller than 5m to the center line of the car and less than 20m ahead of car front. Finally, the possible detection of the cyclist by implemented sensors in the vehicle and the possible triggering of an active safety system like an Automatic Emergency Braking or a Forward Collision Warning are studied. Required detection sensors parameters, such as Field Of View (FOV) and the detection range, were analyzed relatively to the scenario's characteristics, e.g. remaining time after cyclist appearance and before the collision, differences between scenario types. Different sensor FOVs and detection ranges were analyzed to determine their possible rates of cyclist detection. The study concluded that a FOV of 60° and a range of 35m would detect most of the cyclists in car-to-cyclist accident scenarios. It was also concluded that in about 80% of cases, the last time to trigger brake (tLTTB), i.e. the last moment to brake in order to avoid the accident based on physical and comfort braking limitations by the car, was 1s before the collision. It was also calculated that with a FOV of 60°, 51% of cyclists could be detected up to 4s before tLTTB, and 72% up to 1s before tLTTB. Values found in this paper can be useful to determine some of the specifications of an Advanced Driver Assistance System (ADAS), e.g. detection sensor coverages, available time to trigger an autonomous emergency braking or forward collision warning device. Those values are given for general ADAS sensors specification but also per scenario in the case of sensors that can adapt to a specific scenario.

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“…Third, the antenna modules are evaluated for their coverage towards vulnerable road users in front of the car. In [37] the position of cyclists and pedestrians three seconds before impact are quantified. It is found that 90 % of pedestrians are located within ±20 • before impact, and that cyclists come from a wider range with 90 % within ±80 • .…”

Section: Discussionmentioning

confidence: 99%

Automotive Antenna Roof for Cooperative Connected Driving

et al. 2019

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An automotive concept for vehicular communications is proposed that utilizes the potential of the roof area for antennas. Antennas are distributed in cavities and on shelves in the center and on the front and rear roof ends. The arrangement of antennas on the roof allows better radiation to the front and back of automobiles than shark-fins and single cavities. Combining several modules provides space for further antennas, sensors, and integrated front-ends, as well as better spatial separation for multiple-input multiple-output (MIMO) arrays beyond 5G, and cooperative connected and automated driving. A prototype was developed and built into a car chassis. The measured data were analyzed and evaluated in the view of coverage for vulnerable road users and on correlation for the MIMO.

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Time-to-collision analysis of pedestrian and pedal-cycle accidents for the development of autonomous emergency braking systems

Cited by 38 publications

References 4 publications

Car-to-cyclist forward collision warning effectiveness evaluation: a parametric analysis on reconstructed real accident cases

Car-to-cyclist forward collision warning effectiveness evaluation: a parametric analysis on reconstructed real accident cases

Analysis of pre-crash characteristics of passenger car to cyclist accidents for the development of advanced drivers assistance systems

Automotive Antenna Roof for Cooperative Connected Driving

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