A deep learning-based binocular perception system

An obstacle perception system for intelligent vehicle is proposed.The proposed system combines the stereo version technique and the deep learning network model,and is applied to obstacle perception tasks in complex environment.In this paper,we provide a complete system design project,which includes the hardware parameters,software framework,algorithm principle,and optimization method.In addition,special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application.The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles,and suitable for different weather and lighting conditions in complex environment.It announces that the proposed system is flexible and robust to the intelligent vehicle.

Different people,different wild:Comparing and mapping wilderness representation in Wuyishan National Park,China

It is essential to better integrate wilderness representations of different stakeholders into wilderness conservation.The way in which local residents and other stakeholders frame the construction of wilderness of protected areas in developing countries are poorly understood.In these areas,land use policy and decision may lead to conflicts.This study aims to explore existing public wilderness representations using a questionnaire survey(n=514)administered amongst tourists and other stakeholders in the Wuyishan National Park,in southeast China.The spatial differences in public representations of wilderness across different stakeholder groups were compared against expert knowledge.We found that integrated wilderness representation maps of different stakeholder groups were consistent,namely'area where wild animals live','area with no human influence','a barren and lonely area'.However,three sub-representations of the individual stakeholders varied significantly.Moreover,expert-based wilderness mapping did not reflect public representations accurately,and an integrated wilder-ness quality map considering wilderness representations across both stakeholders and experts can better identify detailed wilderness areas.Our study provides new insights and technical support for future exploration of wilder-ness conservation and mapping in China and other countries with insufficient awareness of wilderness values and investigations in a regional scale.

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive Kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.

Potential effects of the nursing work environment on the work-family conflict in operating room nurses

BACKGROUND The nursing working environment is an important subsystem in the hospital environment.A good working environment could have a positive impact on nurses.However,the work-family conflict and unsatisfactory working environment could significantly reduce their working enthusiasm,efficacy as well as the overall quality of the nursing,increase their fatigue,and thereby compromise their career status.AIM To explore the possible status quo and to analyze the correlation between work environment perception and the work-family conflict among nurses in the operating room.METHODS A total of 312 operating room nurses from two first-class hospitals at Grade 2 and two first-class hospitals at Grade 3 in China from May to September 2017 were included in this research using the cluster sampling method.The data,including the general information questionnaire,the practice environment scale of the nursing work index(PES-NWI),and the work-family conflict scale,were systematically collected.Pearson correlation analysis was applied to analyze the correlation between the two scores,with influencing factors analyzed by hierarchical regression analysis.RESULTS A total of 312 questionnaires were issued,and the response rate and effective questionnaire rate were both 96.15%(300/312).The total scores of the PES-NWI scale and the work-family conflict scale were 3.07±0.43(vs maximum up to 4 points)and 52.32±8.79(vs maximum up to 90 points),respectively.The scores of the PES-NWI scale were negatively correlated with that of work-family conflict scale(all P<0.05).The perception of the nursing work environment and the number of night shifts per month were the major factors contributing to the workfamily conflict(all P<0.05).CONCLUSION The nursing work environment and the work-family conflict among nurses in the operating room were both found at a medium level with a negative correlation between the two.

Towards Autonomous Vehicles with Advanced Sensor Solutions

Professional truck drivers are an essential part of transportation in keeping the global economy alive and commercial products moving. In order to increase productivity and improve safety, an increasing amount of automation is implemented in modern trucks. Transition to automated heavy good vehicles is intended to make trucks accident-free and, on the other hand, more comfortable to drive. This motivates the automotive industry to bring more embedded ICT into their vehicles in the future. An avenue towards autonomous vehicles requires robust environmental perception and driver monitoring technologies to be introduced. This is the main motivation behind the DESERVE project. This is the study of sensor technology trials in order to minimize blind spots around the truck and, on the other hand, keep the driver’s vigilance at a sufficiently high level. The outcomes are two innovative truck demonstrations: one R & D study for bringing equipment to production in the future and one implementation to the driver training vehicle. The earlier experiments include both driver monitoring technology which works at a 60% - 80% accuracy level and environment perception (stereo and thermal cameras) whose performance rates are 70% - 100%. The results are not sufficient for autonomous vehicles, but are a step forward, since they are in-line even if moved from the lab to real automotive implementations.

A Review of Testing Object‑Based Environment Perception for Safe Automated Driving

Safety assurance of automated driving systems must consider uncertain environment perception.This paper reviews litera-ture addressing how perception testing is realized as part of safety assurance.The paper focuses on testing for verification and validation purposes at the interface between perception and planning,and structures the analysis along the three axes(1)test criteria and metrics,(2)test scenarios,and(3)reference data.Furthermore,the analyzed literature includes related safety standards,safety-independent perception algorithm benchmarking,and sensor modeling.It is found that the realiza-tion of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

Speech endpoint detection in low-SNRs environment based on perception spectrogram structure boundary parameter

The Perception Spectrogram Structure Boundary（PSSB）parameter is proposed for speech endpoint detection as a preprocess of speech or speaker recognition.At first a hearing perception speech enhancement is carried out.Then the two-dimensional enhancement is performed upon the sound spectrogram according to the difference between the determinacy distribution characteristic of speech and the random distribution characteristic of noise.Finally a decision for endpoint was made by the PSSB parameter.Experimental results show that,in a low SNR environment from-10 dB to 10 dB,the algorithm proposed in this paper may achieve higher accuracy than the extant endpoint detection algorithms.The detection accuracy of 75.2%can be reached even in the extremely low SNR at-10 dB.Therefore it is suitable for speech endpoint detection in low-SNRs environment.

Driving Space for Autonomous Vehicles

Driving space for autonomous vehicles(AVs)is a simplified representation of real driving environments that helps facilitate driving decision processes.Existing literatures present numerous methods for constructing driving spaces,which is a fundamental step in AV development.This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions:how to reconstruct the driving environment,and how to make driving decisions within the constructed driving space.Furthermore,the advantages and disadvantages of different types of driving space are analyzed.The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.

A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment

Purpose–The purpose of this paper is to accurately capture the risks which are caused by each road user in time.Design/methodology/approach–The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment.Firstly,they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory.This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception.The information of object type,position and velocity was accurately obtained.Then,they conducted several experiments in real dense traffic environment on highways and urban roads,which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios.By analyzing the generation process of traffic risks between vehicles and the road environment,the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.Findings–The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking,and the road traffic risk was described as afield of equivalent force.The results extend the understanding of the traffic risk,which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.Originality/value–This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception.The information of object type,position and velocity was used to reduce erroneous data association between tracks and detections.Then,the authors conducted several experiments in real dense traffic environment on highways and urban roads,which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios.By analyzing the generation process of traffic risks between vehicles and the road environment,the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

3D semantic map construction based on point cloud and image fusion

Accurate and robust positioning and mapping are the core functions of autonomous mobile robots,and the ability to analyse and understand scenes is also an important criterion for the intelligence of autonomous mobile robots.In the outdoor environment,most robots rely on GPS positioning.When the signal is weak,the positioning error will interfere with the mapping results,making the semantic map construction less robust.This research mainly designs a semantic map construction system that does not rely on GPS signals for large outdoor scenes.It mainly designs a feature extraction scheme based on the sampling characteristics of Livox-AVIA solid-state LiDAR.The factor graph optimisation model of frame pose and inertial measurement unit(IMU)pre-integrated pose,using a sliding window to fuse solid-state LiDAR and IMU data,fuse laser inertial odometry and camera target detection results,refer to the closest point distance and curvature for semantic information.The point cloud is used for semantic segmentation to realise the construction of a 3D semantic map in outdoor scenes.The experiment verifies that laser inertial navigation odometry based on factor map optimisation has better positioning accuracy and lower overall cumulative error at turning,and the 3D semantic map obtained on this basis performs well.