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

Abstract

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.

Evaluation of Automatic Emergency Braking Systems in Two-Wheeler Crash Scenarios

“…Different definitions of FoV appear in existing research. FoV can indicate either the sensor’s total detection field ( 17 , 30 ) or half of the detection field ( 19 , 43 ). In this study, FoV follows the latter definition; therefore, an FoV of 20° corresponds to a total detection field of 40°.…”

Autonomous driving hazard scenario extraction and safety assessment based on crash reports and carla simulation

“…Autonomous vehicle test scenario construction technology is the core of autonomous driving safety test and evaluation. The existing researches mainly focus on two kinds of methods, one is based on combinatorial reasoning [4,5] and the other is data-driven test scenario construction [6][7][8][9][10][11] . Compared with combinatorial reasoning technology, data-driven technology is based on real world data, which is more authentic and applicable.…”

“…For example, Davidse et al [6] analyzed motorcycle collision scenarios based on real-world collisions. Char and Serre [10] used natural driving data to establish a database of risk scenarios involving cyclists. Lenard et al [11] extracted vehicle-to-pedestrian (V2P) pre-collision scenarios and applied them to automatic emergency braking (AEB) pedestrian detection systems to prevent collisions with pedestrians.…”

Comprehensive Analysis of Minibuses Gravity Center: A Post-Production Review for Car Body Industry

“…Sometimes the construction or structure of the vehicle may easily cause stability and maneuverability problems even under normal operating conditions. The problems that arise in these vehicles are mostly caused by various conditions (eg: full load; unsuitable road conditions; conditions during braking, turning or turning maneuvers where instability easily occurs which one of the effects can be rolled over) [2]- [5].…”