Real life safety benefits of increasing brake deceleration in car-to-pedestrian accidents: Simulation of Vacuum Emergency Braking

Jeppsson, Hanna; Östling, Martin; Lübbe, Nils

doi:10.1016/j.aap.2017.12.001

Cited by 38 publications

(26 citation statements)

References 18 publications

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“…In order to evaluate the collision avoidance algorithm proposed in the paper, classic AEB algorithms including Berkley, Mazda, Honda and SeungwukMoon (Fujita et al, 1995;Doi et al, 1994;Seiler et al, 1998;Moon and Yi, 2008) were also tested for comparison. All tests were implemented via the Prescan Simulation Platform (Prescan, 2018), with vehicle maximum deceleration set at 0.8 g (to account for possible unfavorable driving situations, where generally a maximum deceleration of 10 m/s 2 and 8 m/s is assumed for dry and wet roads respectively for AEB systems (Jeppsson et al, 2018)) and road friction coefficient set at 0.8, and using Prescan built-in 2D simple vehicle dynamics model and radar sensor model ("2D simple" refers to "a model that is capable of simulating a car's longitudinal, lateral and roll motion", but neglects vehicle movement in the vertical direction and assumes the front/rear two tires have the same angle and speed like a bicycle, also referred to as bicycle models (Prescan, 2018)). Finally, simulation results of each algorithm under each test scenario were obtained and shown in Fig.…”

Section: Simulation Test and Resultsmentioning

confidence: 99%

A forward collision avoidance algorithm based on driver braking behavior

Xiong

Wang

Cai

et al. 2019

Accident Analysis & Prevention

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Measuring risk is critical for collision avoidance. The paper aims to develop an online risk level classification algorithm for forward collision avoidance systems. Assuming risk levels are reflected by braking profiles, deceleration curves from critical evasive braking events from the Virginia "100-car" database were first extracted. The curves are then clustered into different risk levels based on spectrum clustering, using curve distance and curve changing rate as dissimilarity metrics among deceleration curves. Fuzzy logic rules of safety indicators at critical braking onset for risk classification were then extracted according to the clustered risk levels. The safety indicators include time to collision, time headway, and final relative distance under emergency braking, which characterizes three kinds of uncertain critical conditions respectively. Finally, the obtained fuzzy risk level classification algorithm was tested and compared with other Automatic Emergency Braking (AEB) algorithms under Euro-NCAP testing scenarios in simulation. Results show the proposed algorithm is promising in balancing the objectives of avoiding collision and reducing interference with driver's normal driving compared with other algorithms.

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Section: Simulation Test and Resultsmentioning

confidence: 99%

A forward collision avoidance algorithm based on driver braking behavior

Xiong

Wang

Cai

et al. 2019

Accident Analysis & Prevention

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“…In addition, vehicles undertaking deceleration level about 1g configure a frequent driving behavior in the real world, during the life cycle of the seat Jeppsson et al (2018). Consequently, fatigue life cycle analysis of the seat anchorage mechanism seems quite appropriate, because it is subjected to variable loading.…”

Section: Fatigue Life Analysismentioning

confidence: 99%

Assessing the crashworthiness of a vehicle seat for rear and frontal impacts

Silva

Flávio

Oliveira

2019

Lat. Am. j. solids struct.

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A vehicle seat represents an automotive passive system because it limits the damage caused to passengers in the events of an accident or a crash. This way, the present work intends to evaluate the safety of an anchorage mechanism of a frontal vehicle driver seat considering two hazardous situations rear and frontal impacts. Performing a nonlinear FE in static and transient analyses provide the stress distribution in the main components of the mechanism, such as the inner track, the lock device and the bolts using Ansys ®. Regulation Nº 17 from Un/ECE regulates the acceleration levels predicted in a crash test and therefore applied in the simulation. In addition, departing from a crash of 5g a fatigue analysis is carried out. From this particular analysis, it becomes possible to evaluate the actual safety of the vehicle seat after a minor crash.

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“…Global Status Report on Road Safety is released by World Health Organization (WHO, Geneva 27, Switzerland) 2018, in which WHO claims that about 1.35 million people die each year in road traffic accidents [ 1 , 2 ]. Similarly, American Automobile Association (AAA) Foundation released press report in 2016 that 50,658 vehicle roads accidents occurred only in America from the year 2011 to 2014 due to roadway obstacles.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Haris

Hou

2020

Sensors

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Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

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Real life safety benefits of increasing brake deceleration in car-to-pedestrian accidents: Simulation of Vacuum Emergency Braking

Cited by 38 publications

References 18 publications

A forward collision avoidance algorithm based on driver braking behavior

A forward collision avoidance algorithm based on driver braking behavior

Assessing the crashworthiness of a vehicle seat for rear and frontal impacts

Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

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