Automotive Innovation

Experimental and Numerical Study of Cervical Muscle Contraction in Frontal Impact

Zhenhai Gao, Zhao Li, Hongyu Hu, Fei Gao

2019, 2(2): 93-101. doi:10.1007/s42154-019-00060-6

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In a crash situation, drivers typically make evasive maneuvers before an upcoming impact, which can affect the kinematics and injury during impact. The purpose of the current study was to investigate the response and effect of drivers’ cervical muscles in a frontal impact. A crash scenario was developed using a vehicle driving simulator, and 10 volunteers were employed to drive the simulator at 20 km/h, 50 km/h, 80 km/h and 100 km/h. Electromyography (EMG) was recorded from the sternocleidomastoideus (SCM), splenius cervicis (SPL) and trapezium (TRP) muscles using a data acquisition system, and the level of muscle activation was calculated. A numerical study was conducted using data collected in the experiment. The results revealed that the cervical muscles were activated during drivers’ protective action. EMG activity of cervical muscles before impact was greater than that during normal driving. EMG activity increased with driving speed, with the SCM and TRP exhibiting larger increases than the SPL. The kinematics and load of the driver were influenced by muscle activation. Before the collision, the head of an active model stretched backward, while the passive model kept the head upright. In low-speed impact, the torque and shear of the cervical muscle in the active model were much lower than those in the passive model, while the tension of the cervical muscle was higher in the active model compared with the passive model. The results indicated that the incidence of cervical injury in high-speed impact is complex.

A Review of Engine Fuel Injection Studies Using Synchrotron Radiation X-ray Imaging

Zhijun Wu, Wenbo Zhao, Zhilong Li, Jun Deng, Zongjie Hu, Liguang Li

2019, 2(2): 79-92. doi:10.1007/s42154-019-00056-2

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Fuel spray characteristics directly determine the formation pattern and quality of the fuel/air mixture in an engine, and thus affect the combustion process. For this reason, the improvement and optimization of fuel injection systems is crucial to the development of engine technologies. The fuel jet breakup and atomization process is a complex liquid–gas two-phase turbulent flow system that has not yet been fully elucidated. Owing to the limitations of standard optical measurement techniques, the spray breakup mechanism and its interaction with the nozzle internal flow are still unclear. However, in recent years synchrotron radiation (SR) X-ray imaging technologies have been widely applied in engine fuel injection studies because of the higher energy and brilliance of third-generation SR light sources. This review provides a brief introduction to the development of SR technology and compares the critical parameters of the primary third-generation SR light sources available worldwide. The basic principles and applications of various X-ray imaging technologies with regard to nozzle internal structure measurements, visualization of in-nozzle flow characteristics and quantitative analyses of near-field spray transient dynamics are examined in detail.

Driving-Cycle-Aware Energy Management of Hybrid Electric Vehicles Using a Three-Dimensional Markov Chain Model

Bolin Zhao, Chen Lv, Theo Hofman

2019, 2(2): 146-156. doi:10.1007/s42154-019-00059-z

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This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy management strategy. The impacts of different prediction time lengths on driving cycle generation were explored. The results indicate that the original driving cycle is compressed by 50%, which significantly reduces the computational burden while having only a slight effect on the prediction performance. The developed driving cycle prediction method was implemented in a real-time energy management algorithm with a hybrid electric vehicle powertrain model, and the model was verified by simulation using two different testing scenarios. The testing results demonstrate that the developed driving cycle prediction method is able to efficiently predict future driving tasks, and it can be successfully used for the energy management of hybrid electric vehicles.

Impact Resistance of Spark Plug’s Ceramic Insulator During Ultra-high-Pressure Combustion Under Deto-Knock Conditions

Yunliang Qi, Boyuan Wang, Zhi Wang

2019, 2(2): 137-145. doi:10.1007/s42154-019-00054-4

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The ceramic insulators of spark plugs in gasoline engines are especially prone to damage when deto-knock occurs. To understand the damage process and mechanism, the present work investigated the impact resistance of ceramic insulators using detonation waves as impact sources. A test device that generates detonation waves was developed, representing a novel means of evaluating the knock resistance of ceramic insulators. Various impact types and detonation intensities were employed, and detonation initiation and propagation at peak pressures greater than 100 MPa were assessed using synchronous high-speed direct photography and pressure measurements. The test results demonstrate that ceramic insulators tend to break at the base of the breathing chamber when damaged by a single high peak pressure detonation wave impact. In contrast, multiple low pressure impacts eventually break the insulator into multiple fragments. The data also show that the positioning of a ground electrode upstream of the ceramic insulator greatly increases the resistance of the ceramic to the detonation impact. A two-dimensional computational fluid dynamics simulation coupled with a chemical kinetics analysis demonstrated that this improved resistance can be ascribed to a reduced peak pressure that appears after the detonation wave diffracts from the electrode prior to contacting the ceramic insulator.

End-to-End Self-Driving Using Deep Neural Networks with Multi-auxiliary Tasks

Dan Wang, Junjie Wen, Yulong Wang, Xiangdong Huang, Feng Pei

2019, 2(2): 127-136. doi:10.1007/s42154-019-00048-2

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End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network (CNN). Although prediction of steering angle has achieved good result in single task, the current approach does not effectively simultaneously predict the steering angle and the speed. In this paper, various end-to-end multi-task deep learning networks using deep convolutional neural network combined with long short-term memory recurrent neural network (CNN-LSTM) are designed and compared, which could obtain not only the visual spatial information but also the dynamic temporal information in the driving scenarios, and improve steering angle and speed predictions. Furthermore, two auxiliary tasks based on semantic segmentation and object detection are proposed to improve the understanding of driving scenarios. Experiments are conducted on the public Udacity dataset and a newly collected Guangzhou Automotive Cooperate dataset. The results show that the proposed network architecture could predict steering angles and vehicle speed accurately. In addition, the impact of multi-auxiliary tasks on the network performance is analyzed by visualization method, which shows the salient map of network. Finally, the proposed network architecture has been well verified on the autonomous driving simulation platform Grand Theft Auto V (GTAV) and experimental road with an average takeover rate of two times per 10 km.

Scale Consistency Quantification for Subjective Evaluation of Vehicle Dynamics

Wan’an Yang

2019, 2(2): 121-126. doi:10.1007/s42154-019-00062-4

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The subjective evaluation of vehicle dynamics performance is widely applied in different stages of vehicle development. However, the rating result has frequently been challenged because it is easily affected by various subjective and objective factors. Currently, there is no suitable index for determining evaluator’s consistency when performing a subjective evaluation of vehicle dynamics. This evaluation is quite unique, with limited samples, multiple indices, and poor repeatability, in addition to being stratified and two dimensional. The cross-grouped factor analysis (CFA) method is proposed to identify the scale for a subjective evaluation and to quantify its consistency. An application case study revealed that the proposed method is effective.

Concept Study of a Self-localization System for Snow-Covered Roads Using a Four-Layer Laser Scanner

Tetsushi Mimuro, Naoya Taniguchi, Hiroyuki Takanashi

2019, 2(2): 110-120. doi:10.1007/s42154-019-00061-5

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Many advanced driver assistance systems have entered the market, and automated driving technologies have been developed. Many of them may not work in adverse weather conditions. A forward-looking camera, for example, is the most popular system used for lane detection but does not work for a snow-covered road. The present paper proposes a self-localization system for snowy roads when the roadsides are covered with snow. The system employs a four-layer laser scanner and onboard sensors and uses only pre-existing roadside snow poles provided for drivers in a snowy region without any other road infrastructure. Because the landscape greatly changes in a short time during a snowstorm and snow removal works, it is necessary to restrict the landmarks used for self-localization to tall objects, like snow poles. A system incorporating this technology will support a driver’s efforts to keep to a lane even in a heavy snowstorm.

Integrated Spacing Policy Considering Micro- and Macroscopic Characteristics

Xinjie Zhang, Yiqing Huang, Konghui Guo, Tao Peng, Shengli Sun, Wentao Li

2019, 2(2): 102-109. doi:10.1007/s42154-019-00049-1

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An appropriate spacing policy improves traffic flow and traffic efficiency while reducing commuting time and energy consumption. In this paper, the integrated spacing policy that combines the benefits of the constant time headway (CTH) and safety distance (SD) spacing policies is proposed in an attempt to improve traffic flow and efficiency. Firstly, the performance of the CTH and SD spacing policies is analyzed from the perspective of the microscopic characteristics of human-vehicle and the macroscopic characteristics of traffic flow. The switching law between CTH and SD spacing policies and the integrated spacing policy are then proposed to increase traffic efficiency according to the traffic conditions, and the critical speed for the proposed integrated spacing policy is derived. Using the proposed switching law, the integrated spacing policy utilizes the safety redundancy difference between the CTH and SD spacing policies in a flexible manner. Simulation tests demonstrate that the proposed integrated spacing policy increases traffic flow and that the traffic flow maintains string stability in a wider range of traffic flow density.