scholarly journals Automatic Emergency Braking (AEB) System Impact on Fatality and Injury Reduction in China

2020 ◽  
Vol 17 (3) ◽  
pp. 917 ◽  
Author(s):  
Hong Tan ◽  
Fuquan Zhao ◽  
Han Hao ◽  
Zongwei Liu ◽  
Amer Ahmad Amer ◽  
...  
Keyword(s):  
Road Traffic ◽  
Penetration Rate ◽  
Evaluation Model ◽  
National Level ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Market Penetration ◽  
Emergency Braking ◽  
Activation Rate ◽  
Potential Maximum

The automatic emergency braking (AEB) system is an effective intelligent vehicle active safety system for avoiding certain types of collisions. This study develops a national-level safety impact evaluation model for this intelligent vehicle function, including the potential maximum impact and realistic impact. The evaluation model was firstly applied in China to provide insights into Chinese policymaking. Road traffic fatality and severe injury trends, the proportion of different collision types, the effectiveness of collision avoidance, and the AEB market penetration rates are considered in the potential maximum impact scenario. Furthermore, the AEB activation rate and the technology’s technical limitations, including its effectiveness in different weather, light, and speed conditions, are discussed in the realistic scenario. With a 100% market penetration rate, fatalities could be reduced by 13.2%, and injuries could be reduced by 9.1%. Based on China’s policy, the market penetration rate of intelligent vehicles with AEB is predicted to be 34.0% in 2025 and 60.3% in 2030. With this large market penetration rate increase of AEB, the reductions in fatalities and severe injuries are 903–2309 and 2025–5055 in 2025; and 1483–3789 and 3895–7835 in 2030, respectively. Considering AEB’s activation rate and its three main limitations, the adjusted realistic result is approximately 2/5 of the potential maximum result.

Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

2019 ◽  
Vol 2673 (9) ◽  
pp. 430-439 ◽  
Author(s):  
Hwapyeong Yu ◽  
Sehyun Tak ◽  
Minju Park ◽  
Hwasoo Yeo
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Positive Impact ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Market Penetration ◽  
Mixed Traffic ◽  
Negative Effects ◽  
Average Speed ◽  
Time To Collision

The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AVs in mixed traffic and in a situation where an AV-only lane is deployed. In the analysis, we investigate the average speed, the throughput, and the inverse time-to-collision (ITTC). We differentiate the behaviors of manual vehicles and AVs through the reaction time, desired speed, and car-following models. As a result, we observe that the efficiency is improved when the market penetration rate of AVs increases, especially when the highway throughput increases by up to 84% in the case of mixed traffic. However, safety worsens when the market penetration of AVs is under 40%. In this case, the average speed can be improved and the frequency of dangerous situations (ITTC > 0.49) can be reduced drastically in the merging section by making the innermost lane AV-only. Accordingly, we conclude that AV-only lanes can have a significant positive impact on efficiency and safety when the market penetration rate of AVs is low.

scholarly journals Sensor-Based Environmental Perception Technology for Intelligent Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Biyao Wang ◽  
Yi Han ◽  
Di Tian ◽  
Tian Guan
Keyword(s):  
Control Strategies ◽  
Research Direction ◽  
Environmental Perception ◽  
Intelligent Vehicles ◽  
Lane Detection ◽  
Intelligent Vehicle ◽  
Cruise Control ◽  
Emergency Braking ◽  
Decision Control ◽  
Wave Radar

Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

scholarly journals Key points of investigation and analysis on traffic accidents involving intelligent vehicles

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Quan Yuan ◽  
Yong Peng ◽  
Xiaodong Xu ◽  
Xinghua Wang
Keyword(s):  
Automobile Industry ◽  
Road Traffic ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Accident Investigation ◽  
Traffic System ◽  
Vehicle Accidents ◽  
The Road ◽  
Vehicle Accident ◽  
Key Points

Abstract Intellectualization is regarded as the future mainstream development trend of the automobile industry. The automation level of intelligent vehicles is relatively low so far, and the road traffic system will be in a mixed state of non-autonomous vehicles and vehicles with different levels of automation for a long time. Therefore, the road traffic system will be more complex with more diverse accidents. This paper analysed the characteristics and causal factors of intelligent vehicle accidents. Based on the problems existing in investigation, analysis and liability identification of intelligent vehicle accident, the study proposed a preliminary accident investigation framework and method, summarized the key points of accident analysis from the perspectives of technical defects, information security and passive safety, and specified the liability subjects for intelligent vehicle accidents and their corresponding legal liability. The results from this study contributed to the development of intelligent vehicle accident investigation and disposal, and provided the reference for the improvement of vehicle safety and accident prevention.

Road Traffic Sign Identification in Weak Illumination for Intelligent Vehicle Based on Machine Vision

2018 ◽  
Vol 11 (2) ◽  
pp. 127-134 ◽  
Author(s):  
Zhaofei Huang ◽  
Shian Qiu ◽  
Jun Li ◽  
Yibing Zhao ◽  
Pan Cui ◽  
...  
Keyword(s):  
Machine Vision ◽  
Road Traffic ◽  
Intelligent Vehicle ◽  
Traffic Sign

scholarly journals Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling

2021 ◽  
Vol 18 (11) ◽  
pp. 5604
Author(s):  
Yookyung Boo ◽  
Youngjin Choi
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
National Level ◽  
Brier Score ◽  
Support Vector ◽  
Main Diagnosis ◽  
Road Traffic Accidents ◽  
Multiple Variables ◽  
Type Of Injury ◽  
Kernel Model

In this study, four models—logistic regression (LR), random forest (RF), linear support vector machine (SVM), and radial basis function (RBF)-SVM—were compared for their accuracy in determining mortality caused by road traffic injuries. They were tested using five years of national-level data from the Korea Disease Control and Prevention Agency’s (KDCA) National Hospital Discharge In-Depth Survey (2013 through to 2017). Model performance was measured for accuracy, precision, recall, F1 score, and Brier score metrics using classification analysis that included characteristics of patients, accidents, injuries, and illnesses. Due to the number of variables and differing units, the rates of survival and mortality related to road traffic accidents were imbalanced, so the data was corrected and standardized before the classification models’ performances were compared. Using the importance analysis, the main diagnosis, the type of injury, the site of the injury, the type of injury, the operation status, the type of accident, the role at the time of the accident, and the sex were selected as the analysis factors. The biggest contributing factor was the role in the accident, which is the driver, and the major sites of the injuries were head injuries and deep injuries. Using selected factors, comparisons of the classification performance of each model indicated RBF-SVM and RF models were superior to the others. Of the SVM models, the RBF kernel model was superior to the linear kernel model; it can be inferred that the performance of the high-dimensional transformed RBF model is superior when the dimension is complex because of the use of multiple variables. The findings suggest there are limitations to analyses involving imbalanced, multidimensional original data, such as data on road traffic mortality. Thus, analyses must be performed after imbalances are corrected.

scholarly journals High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian
Keyword(s):  
High Resolution ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Data Driven ◽  
Traffic Information ◽  
Flow Density ◽  
Massive Data ◽  
Market Penetration ◽  
High Resolution Data ◽  
Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

Mitigation of road traffic crash hazard in Pakistan

2014 ◽  
Vol 23 (5) ◽  
pp. 567-585
Author(s):  
Muhammad Masood Rafi ◽  
Ashar Hashmat Lodi ◽  
Muhammad Arsalan Effendi
Keyword(s):  
Road Traffic ◽  
Evaluation Model ◽  
Road Traffic Crash ◽  
Potential Hazard ◽  
Suggested Model ◽  
Male Population ◽  
Content Type ◽  
The Road ◽  
On The Road ◽  
The City

Purpose – Road traffic crashes (RTCs) result in creating significant social and economic hazard for affectees, their families and society. The purpose of this paper is to present studies which were conducted to study the patterns of RTCs in Karachi which is a metropolitan city of Pakistan. The studies were conducted on one of the busiest roads in the city named as Shara-e-Faisal. The influence and contribution of different factors in RTCs has been studied and hazardous road sections of Shara-e-Faisal have been identified. Based on the data analysis, an evaluation model has been suggested to reduce the hazard of RTCs on Shara-e-Faisal. The objective of the presented studies is to increase the present level of safety of road travel by reducing crashes on Shara-e-Faisal. Design/methodology/approach – Existing data of RTCs in Karachi have been analysed for the presented studies. For this purpose, Shara-e-Faisal was divided in sections of 1 km length to study the vehicle crash pattern. Location surveys were conducted to record physical conditions of this road. A cluster analysis was carried out to identify hazardous sections of the road. An evaluation model has been suggested in the end to reduce the hazard of RTCs by identifying hazardous road sections of Shara-e-Faisal. Findings – The analysis of the data revealed that the crashes were higher over weekend and on Monday. Male population, particularly young people, and motorcycle riders were the largest affectees of RTCs. In general, more daytime crashes were recorded as compared to nighttime crashes. The crashes in the mid block of the road and those involving rear-end collisions were higher. The hazardous road locations were related to poor road conditions. Statistical analysis indicated that alternate routes were required to reduce the RTC hazard on Shara-e-Faisal. Research limitations/implications – The paper is a small, but an original, contribution to identify a potential hazard which is faced by the community in the city. This is the first attempt (to the best of authors’ knowledge) to address the issue of RTCs in Karachi from an engineering view point. Practical implications – The suggested model can be employed by the authorities as a guideline to mitigate the hazard of road crashes in the country. Originality/value – The paper provides valuable information on the road traffic incidents, their pattern and contributing factors in one of the largest metropolis of Pakistan. The suggested model can become helpful in reducing RTCs in Pakistan.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

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