Research on X-by-Wire Chassis Technology for Intelligent Driving of New Energy Vehicles

As China’s economy develops,new energy technologies and intelligent driving have become a trend in the automobile industry.The development of new energy vehicles has accelerated,with X-by-wire chassis technology becoming the core technology for intelligent driving.This technology includes steer-,brake-,shift-,and throttle-by-wire systems.It is not only the key technology for new energy vehicles but also an important support for promoting their sustainable development.This article presents an in-depth study on X-by-wire chassis technology in new energy vehicles and its basic working principle.

A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain

The type, model, quantity, and location of sensors installed on the intelligent vehicle test platform are different, resulting in different sensor information processing modules, The driving map used in intelligent vehicle test platform has no uniform standard, which leads to different granularity of driving map information, The sensor information processing module is directly associated with the driving map information and decision-making module, which leads to the interface of intelligent driving system software module has no uniform standard, Based on the software and hardware architecture of intelligent vehicle, the sensor information and driving map information are processed by using the formal language of driving cognition to form a driving situation graph cluster and output to a decision-making module, and the out- put result of the decision-making module is shown as a cognitive arrow cluster, so that the whole process of intelligent driving from perception to decision-making is completed, The formalization of driving cognition reduces the influence of sensor type, model, quantity, and location on the whole software architec- ture, which makes the software architecture portable on different intelligent driving hardware platforms.

CVTD: A Robust Car-Mounted Video Text Detector

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted videos can assist drivers in making decisions.However,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time detection.We proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary shapes.Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD model.The enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text regions.Additionally,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s performance.We further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection speed.This model holds potential for practical applications in real-world scenarios.

Automated identification of fissure trace in mining roadway via deep learning

The fractured rocks distributed near the structural surface of coal roadway heading face can easily cause water or air conduits,ultimately resulting in accidents that threaten production safety,such as mine flooding,gas leakage,or supporting failure.Therefore,successful detection of fissures is crucial to mine safety.Despite the rising popularity of computer vision in accurate fissures detection,it fails to satisfy the demand of engineering practice.To address this problem,this paper first establishes a 1000-image database of fissures of coal roadway heading face based on data collection,cleaning,and annotation.Then,a framework for fissure detection and segmentation is constructed with deep convolutional neural network(DCNN)named DeepLabv3+ serving as the overall architecture,and a lightweight MobileNetV2,instead of the original Xception,as the main feature extraction network.The database is then employed to train and test the neural network model.Finally,the robustness and adaptability of the model under the common jamming environment in coal mines are evaluated.According to the results,the deep learning algorithm,which performs favorably in identifying various fissures in the coal roadway heading face,is immune to interference such as low illumination,wire mesh,multi-scale edge,cutting mark,and concentrated light beams.Notably,the performance of such method is on par with,or better than,humans in performing individual image segmentation.The approach performs higher segmentation accuracy and calculation speed than the traditional image identification algorithm,thus realizing rapid identification of fissures in coal mines in batch.This study can provide a reference for image semantic segmentation of fissure traces under similar conditions.

Research on the Mechanism of Multi-Sensor Fusion Configuration Based on the Optimal Principle of the Vehicle

In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.

An analysis on possibility of intelligent speed adaptation in terms of drivers' consciousness

Intelligent speed adaptation （ISA） is considered as an effective measure to reduce number of traffic accidents in the field of intelligent transportation systems （ITS）. On the other hand, its effects for traffic safety are still doubted by many people. To make the possibility analysis, an experiment is conducted by using driving simulator. Regarding ISA ap- proaches, there are three modes： mandatory, voluntary and advisory. Among them, the advisory type seems to be the easiest one to introduce. Therefore, we focus on the advisory mode in this study by considering ISA just at the beginning stage in Japan. The experiment consists of four steps： without ISA, ISA using pictures, ISA using voices and again without ISA. The outputs obtained from the driving simulator are analyzed combined with the consciousness of the participants. The experiment shows that the ISA can improve recognition of speed limitation especially for people who have random rambling or looking aside tendency. Furthermore, the ISA especially when using voices can contribute in changing the consciousness of people who are aggressive in driving. Their driving speeds can reduce so that positive effects on traffic safety can be concluded.

Preparation and performance analysis of Pt-IPMC for driving bionic tulip

Based on the biological characteristics of tulip,the low driving voltage and fast response of ionic polymer metal composite(IPMC),we analyzed the fabrication,morphology and performance of the platinum IPMC(Pt-IPMC)and selected the right IPMC for driving bionic tulip.The preparation and performance of IPMC was analyzed first in this paper such as blocking force,output displacement and bending angle of IPMC under the different directed current voltage(DC).The optimal IPMC sample size and driving voltage were selected based on tulip blooming angles and the strain energy density of IPMC,which completed the blooming process of bionic tulip.The feasibility of IPMC used in driving bionic field was fully proved in this paper,which laid a foundation for the application of IPMC in driving biomimetic biological robots.

Relay navigation strategy study on intelligent drive on urban roads

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.