Sensor-Based Environmental Perception Technology for Intelligent Vehicles

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.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17030917 ◽

2020 ◽

Vol 17 (3) ◽

pp. 917 ◽

Cited By ~ 1

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.

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Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review

Journal of Advanced Transportation ◽

10.1155/2019/9343705 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 8

Author(s):

Wooseok Do ◽

Omid M. Rouhani ◽

Luis Miranda-Moreno

Keyword(s):

Literature Review ◽

Field Data ◽

Adaptive Cruise Control ◽

Intelligent Vehicles ◽

Intelligent Vehicle ◽

Manufacturing Companies ◽

Cruise Control ◽

Car Following ◽

Automated Vehicle ◽

Simulation Based

This study provides a literature review of the simulation-based connected and automated intelligent-vehicle studies. Media and car-manufacturing companies predict that connected and automated vehicles (CAVs) would be available in the near future. However, society and transportation systems might not be completely ready for their implementation in various aspects, e.g., public acceptance, technology, infrastructure, and/or policy. Since the empirical field data for CAVs are not available at present, many researchers develop micro or macro simulation models to evaluate the CAV impacts. This study classifies the most commonly used intelligent-vehicle types into four categories (i.e., adaptive cruise control, ACC; cooperative adaptive cruise control, CACC; automated vehicle, AV; CAV) and summarizes the intelligent-vehicle car-following models (i.e., Intelligent Driver Model, IDM; MICroscopic Model for Simulation of Intelligent Cruise Control, MIXIC). The review results offer new insights for future intelligent-vehicle analyses: (i) the increase in the market-penetration rate of intelligent vehicles has a significant impact on traffic flow conditions; (ii) without vehicle connections, such as the ACC vehicles, the roadway-capacity increase would be marginal; (iii) none of the parameters in the AV or CAV models is calibrated by the actual field data; (iv) both longitudinal and lateral movements of intelligent vehicles can reduce energy consumption and environmental costs compared to human-driven vehicles; (v) research gap exists in studying the car-following models for newly developed intelligent vehicles; and (vi) the estimated impacts are not converted into a unified metric (i.e., welfare economic impact on users or society) which is essential to evaluate intelligent vehicles from an overall societal perspective.

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Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

Remote Sensing ◽

10.3390/rs13061064 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1064

Author(s):

Zhangjing Wang ◽

Xianhan Miao ◽

Zhen Huang ◽

Haoran Luo

Keyword(s):

Millimeter Wave ◽

Autonomous Vehicles ◽

Texture Features ◽

Heterogeneous Data ◽

Environmental Perception ◽

Radar Signal ◽

Time Frequency ◽

Fusion Data ◽

Wave Radar ◽

Automation Equipment

The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.

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Implementation and vehicle tests of a vehicle stop-and-go cruise control system

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1243/095440702760178479 ◽

2002 ◽

Vol 216 (7) ◽

pp. 537-544 ◽

Cited By ~ 13

Author(s):

K Yi ◽

N Ryu ◽

H J Yoon ◽

K Huh ◽

D Cho ◽

...

Keyword(s):

Control System ◽

Control Algorithm ◽

Speed Control ◽

Step Motor ◽

Cruise Control ◽

Longitudinal Control ◽

Linear Quadratic Optimal Control ◽

Millimetre Wave ◽

Stop And Go ◽

Wave Radar

Implementation and vehicle tests of a vehicle longitudinal control algorithm for stop-and-go cruise control have been performed. The vehicle longitudinal control scheme consists of a set-speed control algorithm, a speed control algorithm, and a distance control algorithm. A desired acceleration for the vehicle for the control of vehicle-to-vehicle relative speed and clearance has been designed using linear quadratic optimal control theory. Performance of the control algorithm has been investigated via vehicle tests. Vehicle tests have been conducted using two test vehicles. A 2000 cm3 passenger car equipped with a radar distance sensor, throttle/brake actuators and a controller has been used as a subject vehicle in the vehicle tests. A millimetre wave radar sensor has been used for distance measurement. A step motor and an electronic vacuum booster have been used for throttle/brake actuators. It has been shown that the implemented vehicle longitudinal control system can provide satisfactory performance in vehicle set-speed control and vehicle clearance control at lower speeds.

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

Mathematical Problems in Engineering ◽

10.1155/2010/578406 ◽

2010 ◽

Vol 2010 ◽

pp. 1-20 ◽

Cited By ~ 2

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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Night Vision Pedestrian Warning intelligent vehicle (IV) night vision pedestrian warning in Intelligent Vehicles intelligent vehicle (IV)

Encyclopedia of Sustainability Science and Technology ◽

10.1007/978-1-4419-0851-3_782 ◽

2012 ◽

pp. 6950-6975

Author(s):

Matthias Serfling ◽

Otto Löhlein

Keyword(s):

Intelligent Vehicles ◽

Night Vision ◽

Intelligent Vehicle

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Blockchain-Autonomous Driving Systems

Advances in Data Mining and Database Management - Opportunities and Challenges for Blockchain Technology in Autonomous Vehicles ◽

10.4018/978-1-7998-3295-9.ch006 ◽

2021 ◽

pp. 87-114

Author(s):

P. Lalitha Surya Kumari

Keyword(s):

Autonomous Vehicles ◽

Intelligent Transportation System ◽

Autonomous Driving ◽

Intelligent Vehicles ◽

Intelligent Vehicle ◽

Privacy And Security ◽

Trust Network ◽

Vehicle Communication ◽

Blockchain Technology ◽

Vehicle Data

Blockchain is the upcoming new information technology that could have quite a lot of significant future applications. In this chapter, the communication network for the reliable environment of intelligent vehicle systems is considered along with how the blockchain technology generates trust network among intelligent vehicles. It also discusses different factors that are effecting or motivating automotive industry, data-driven intelligent transportation system (D2ITS), structure of VANET, framework of intelligent vehicle data sharing based on blockchain used for intelligent vehicle communication and decentralized autonomous vehicles (DAV) network. It also talks about the different ways the autonomous vehicles use blockchain. Block-VN distributed architecture is discussed in detail. The different challenges of research and privacy and security of vehicular network are discussed.

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Interval-based/Data-driven Risk Management for Intelligent Vehicles: Application to an Adaptive Cruise Control System

2019 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2019.8814216 ◽

2019 ◽

Author(s):

Nadhir Mansour Ben Lakhal ◽

Lounis Adouane ◽

Othman Nasri ◽

Jaleleddine Ben Hadj Slama

Keyword(s):

Risk Management ◽

Control System ◽

Adaptive Cruise Control ◽

Intelligent Vehicles ◽

Data Driven ◽

Cruise Control ◽

Adaptive Cruise Control System ◽

Cruise Control System

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The Negative Impact of Vehicular Intelligence on Energy Consumption

Journal of Advanced Transportation ◽

10.1155/2019/1521928 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Zongwei Liu ◽

Hong Tan ◽

Xu Kuang ◽

Han Hao ◽

Fuquan Zhao

Keyword(s):

Energy Consumption ◽

Negative Impact ◽

Communication Technologies ◽

High Energy ◽

Intelligent Vehicles ◽

Intelligent Vehicle ◽

Computing Platform ◽

Car Market ◽

Emissions Mitigation ◽

Consumption Cost

The development of intelligent vehicle will provide the Chinese automotive industry with a strategic opportunity for transformation and upgrading. Vehicular intelligence provides new solutions for energy conservation and emissions mitigation. However, the process of vehicular intelligence is progressive. The saving of energy consumption depends on the high smart car market penetration rate. But one thing that can be confirmed is that intelligent vehicles are equipped with advanced sensors, controllers, and actuators, in combination with connecting communication technologies compared with conventional vehicles, for which the energy consumption of the vehicle will definitely increase. In this study, vehicle fuel consumption cost at different levels of intelligence is calculated, considering the energy consumption of hardware used for automation and connecting functions, the energy consumption cost generated by the quality of the hardware, and the wind resistance. The results reveal that the energy consumption per 100 kilometers of an intelligent vehicle ranges from 0.78L to 1.86L, more than traditional vehicle. The energy consumption cost of automation functions is much higher than that of the connecting functions. Computing platform performance, connection strength, and radar performance are the three main factors that affect energy consumption cost. Based on the analysis, the high energy consumption cost of vehicular intelligence has a profound impact on choosing power platform.

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