Automotive radar system for multiple‐vehicle detection and tracking in urban environments

Eltrass, Ahmed S.; Khalil, Mohammed M.

doi:10.1049/iet-its.2017.0370

Cited by 30 publications

(13 citation statements)

References 34 publications

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“…In the same reference [ 79 ], a brief review of the key developments in radar and signal processing techniques applied to the estimation of significant target parameters, such as range, velocity, and direction, are presented with mathematical illustrations. Thanks to extensive use in the automotive industry for various applications and due to advances in signal processing by applying machine learning, pattern recognition techniques, and robust algorithm developments [ 80 ], radar data now have more knowledge of object dimension [ 81 ], object orientation, motion prediction [ 82 ], and classification information [ 83 , 84 , 85 ].…”

Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

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Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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“…Concerning perception, the technology has undergone a great deal of development in recent years. Autonomous vehicles could use a wide variety of sensor systems, such as computer vision [14,15], ultrasonic [16], radar [17] or LiDAR [18,19], among others. Thanks to the advantages and limitations of each one, the current trend is focused on sensor information fusion [20,21].…”

Section: Related Workmentioning

confidence: 99%

Implementation of a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments

Martínez

Jiménez

2019

Sensors

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Autonomous driving is undergoing huge developments nowadays. It is expected that its implementation will bring many benefits. Autonomous cars must deal with tasks at different levels. Although some of them are currently solved, and perception systems provide quite an accurate and complete description of the environment, high-level decisions are hard to obtain in challenging scenarios. Moreover, they must comply with safety, reliability and predictability requirements, road user acceptance, and comfort specifications. This paper presents a path planning algorithm based on potential fields. Potential models are adjusted so that their behavior is appropriate to the environment and the dynamics of the vehicle and they can face almost any unexpected scenarios. The response of the system considers the road characteristics (e.g., maximum speed, lane line curvature, etc.) and the presence of obstacles and other users. The algorithm has been tested on an automated vehicle equipped with a GPS receiver, an inertial measurement unit and a computer vision system in real environments with satisfactory results.

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“…Mm-wave radar sensors have become increasingly popular among automotive radar systems [1], [2]. Among the numerous types of radar sensors, FMCW radars have been the most widely used in automotive mm-wave radar systems in recent years due to their simple radio structure, low power consumption, etc.…”

Section: Introductionmentioning

confidence: 99%

A PELT-KCN Algorithm for FMCW Radar Interference Suppression Based on Signal Reconstruction

Liu

Lü

et al. 2020

IEEE Access

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In frequency-modulated continuous-wave (FMCW) radar interference suppression based on signal reconstruction, the pruned exact linear time (PELT) algorithm is used to detect the time positions of the interference. Due to the uncertain penalty factor of the PELT algorithm, the exactness of the position detection is reduced; thus, the suppression performance is degraded. We propose a PELT algorithm with a known change number (PELT-KCN), where a known change number is used to calculate the optimal penalty factor such that the high accuracy of the algorithm can be guaranteed. After interference recognition, the beat signal is separated into two parts: the undamaged signal and the damaged signal. The former is utilized to restore the latter through an autoregressive (AR) model. In simulations and field experiments, we applied our proposed PELT-KCN algorithm to the interference suppression method and verified its performance. Our method can accurately detect the time positions of interference and effectively improve the signal-to-noise ratio (SNR) of the detected targets.INDEX TERMS Change-point detection, frequency modulated continuous wave (FMCW) radar, mutual interference suppression, signal restoration.

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Automotive radar system for multiple‐vehicle detection and tracking in urban environments

Cited by 30 publications

References 34 publications

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Implementation of a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments

A PELT-KCN Algorithm for FMCW Radar Interference Suppression Based on Signal Reconstruction

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