Multiobjective optimization on cooperative control of autonomous emergency steering and occupant restraint system for enhancing occupant safety

The MADYMO simulation model of the rear occupant restraint system based a current car model was constructed, researching the influence of the rear seat cushion stiffness and cushion angle with the floor, the 3-point seatbelt webbing stiffness and retractor locking feature on rear-seat passenger safety in the head-on collision. Thereafter, the entire rear occupant restraint system was optimised. The result shows when the rear seat cushion stiffness is 30N/mm, the cushion angle is 25°, the safety belt stiffness is 11% and the webbing output after the retractor locks is 30mm, rear occupant would obtain the optimum protection.

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A nonlinear occupant-restraint system model for predicting human injuries caused by vertical impact

Nonlinear Dynamics ◽

10.1007/s11071-021-06490-4 ◽

2021 ◽

Author(s):

Di Zhou ◽

Xianhui Wang ◽

Qichen Zheng ◽

Tiaoqi Fu ◽

Mengyang Wu ◽

...

Keyword(s):

System Model ◽

Restraint System ◽

Occupant Restraint System ◽

Vertical Impact

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Occupant Restraint System Evaluation - Lateral Rollover System-Level Heavy Trucks

10.4271/j2426_202103 ◽

2021 ◽

Author(s):

Keyword(s):

System Level ◽

System Evaluation ◽

Restraint System ◽

Occupant Restraint System ◽

Heavy Trucks

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Research on the Safety of Rear Seat Occupants in the Frontal Collision

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.569.795 ◽

2012 ◽

Vol 569 ◽

pp. 795-799

Author(s):

Liang Hong ◽

Yun Teng Wu

Keyword(s):

Simulation Model ◽

Rear Seat ◽

Performance Criterion ◽

Restraint System ◽

Vehicle Model ◽

Occupant Restraint System ◽

Head Injury Criterion ◽

Frontal Collision ◽

Seat Cushion ◽

Left And Right

To study the injury values rear seat occupants sustain in the frontal collision, this paper constructed the simulation model of the rear occupant restraint system of a vehicle model using MADYMO software. The influence of the rear 3-point seatbelt stiffness and retractor locking feature, the rear seat cushion stiffness and angle with the vehicle floor on head injury criterion HIC36, thorax 3ms resultant acceleration T3MS, thorax performance criterion THPC, left and right femur force of rear occupants were analysed through the simulation model. The conclusion shows that HIC36 drops when the seatbelt stiffness increases and retractor locking feature decreases compared to the original values; HIC36, T3MS, left and right femur force become less when the seat cushion stiffness decreases and angle increases compared to the original values.

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Stochastic Kriging for Crashworthiness Optimization Accounting for Simulation Noise

Volume 11B: 46th Design Automation Conference (DAC) ◽

10.1115/detc2020-22532 ◽

2020 ◽

Author(s):

Seyed Saeed Ahmadisoleymani ◽

Samy Missoum

Keyword(s):

Stochastic Process ◽

Optimization Algorithm ◽

Stationary Stochastic Process ◽

Expected Improvement ◽

Restraint System ◽

Stochastic Kriging ◽

Occupant Restraint System ◽

Crashworthiness Optimization ◽

Surrogate Based Optimization ◽

Available Information

Abstract Vehicle crash simulations are notoriously costly and noisy. When performing crashworthiness optimization, it is therefore important to include available information to quantify the noise in the optimization. For this purpose, a stochastic kriging can be used to account for the uncertainty due to the simulation noise. It is done through the addition of a non-stationary stochastic process to the deterministic kriging formulation. This stochastic kriging, which can also be used to include the effect of random non-controllable parameters, can then be used for surrogate-based optimization. In this work, a stochastic kriging-based optimization algorithm is proposed with an infill criterion referred to as the Augmented Expected Improvement, which, unlike its deterministic counterpart the Expect Improvement, accounts for the presence of irreducible aleatory variance due to noise. One of the key novelty of the proposed algorithm stems from the approximation of the aleatory variance and its update during the optimization. The proposed approach is applied to the optimization of two problems including an analytical function and a crashwor-thiness problem where the components of an occupant restraint system of a vehicle are optimized.

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Development of Occupant Restraint System for SUV - Telco Experience

10.4271/2003-26-0027 ◽

2003 ◽

Author(s):

S. Ravishankar ◽

C. Anil Kumar ◽

B. S. Praveen

Keyword(s):

Restraint System ◽

Occupant Restraint System

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Study on the Difference of Frontal Impact Response among People of Different Sizes

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.111 ◽

2010 ◽

Vol 34-35 ◽

pp. 111-116 ◽

Cited By ~ 1

Author(s):

Li Bo Cao ◽

Chong Zhen Cui ◽

Ning Yu Zhu ◽

Huan Chen

Keyword(s):

Human Body ◽

Injury Risk ◽

Simulation Models ◽

Restraint System ◽

Frontal Impact ◽

Occupant Restraint System ◽

Impact Simulation ◽

The Difference ◽

Human Body Models ◽

Multi Body

In this article, seven frontal impact simulation models with same restraint system and different human body models were established through the use of multi-body kinematics software MADYMO. The injuries in head, chest and femurs of different human models and the differences of these injuries were analyzed in detail. The weighted injury criterion was adopted to evaluate the overall injuries of different human body models. The results shows that the injury risk of smaller human body is much higher than the taller human body, and existing occupant restraint system that protects the 50th percentile American occupant well protects other size occupant poorly.

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