Development of Sensors Testbed for Autonomous Vehicles

Vargas, Jorge; Alsweiss, Suleiman; Jernigan, Michael; Amin, Abu Bony; Brinkmann, Marius; Santos, Joshua; Razdan, Rahul

doi:10.1109/southeastcon42311.2019.9020344

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…By introducing different sequences of chirps with different rates of frequency change, several different chirp sequences can be compared to one another. MFSK radars have three different customized properties to introduce a higher number of unique signals: (1) the utilization of different channels, (2) changing the rate at which frequencies modulate, and (3) the incorporation of multiple differently sloped chirp sequences. These three properties are the basis on which the proposed coding scheme is derived.…”

Section: Signal Parametersmentioning

confidence: 99%

“…Radar sensors will acquire data to improve simultaneous range and Doppler estimation, both of which are crucial for safe navigation. Advanced driver-assistance system (ADAS) has laid out definitions for autonomous driving in six distinct stages as shown in Figure 1 to mark progress towards AV sensor-based driving functions and high-definition maps once vehicles are performing all driving functions [1,2]. Mutual interference is a major issue that prevents AVs from using radar data during periods of high traffic.…”

Section: Introductionmentioning

confidence: 99%

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A Modulated Approach for Improving MFSK RADARS to Resolve Mutual Interference on Autonomous Vehicles (AVs)

Duke

Neville

Vargas

2023

Sensors

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This paper proposes a novel automotive radar waveform involving the theory behind M-ary frequency shift key (MFSK) radar systems. Along with the MFSK theory, coding schemes are studied to provide a solution to mutual interference. The proposed MFSK waveform consists of frequency increments throughout the range of 76 GHz to 81 GHz with a step value of 1 GHz. Instead of stepping with a fixed frequency, a triangular chirp sequence allows for static and moving objects to be detected. Therefore, automotive radars will improve Doppler estimation and simultaneous range of various targets. In this paper, a binary coding scheme and a combined transform coding scheme used for radar waveform correlation are evaluated in order to provide unique signals. AVs have to perform in an environment with a high number of signals being sent through the automotive radar frequency band. Efficient coding methods are required to increase the number of signals that are generated. An evaluation method and experimental data of modulated frequencies as well as a comparison with other frequency method systems are presented.

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Section: Signal Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modulated Approach for Improving MFSK RADARS to Resolve Mutual Interference on Autonomous Vehicles (AVs)

Duke

Neville

Vargas

2023

Sensors

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“…Adaptive cruise control (ACC), collision avoidance systems (CAS), blind spot detection (BSD), and lane change assist (LCA) are some examples. Matlab was used to create the initial step in establishing a complete testbed for AV sensor testing and verification, with an emphasis on radar systems under all environmental conditions of relevance [ 12 ].…”

Section: Autonomous Vehicles Sensors Ecosystemmentioning

confidence: 99%

An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions

Vargas

Alsweiss

Toker

et al. 2021

Sensors

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185

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Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors’ operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.

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A Review of Intelligent Connected Vehicle Cooperative Driving Development

et al. 2022

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With the development and progress of information technology, especially V2X technology, the research focus of intelligent vehicles gradually shifted from single-vehicle control to multi-vehicle control, and the cooperative control system of intelligent connected vehicles became an important topic of development. In order to track the research progress of intelligent connected vehicle cooperative driving systems in recent years, this paper discusses the current research of intelligent connected vehicle cooperative driving systems with vehicles, infrastructure, and test sites, and analyzes the current development status, development trend, and development limitations of each object. Based on the analysis results of relevant references of the cooperative control algorithm, this paper expounds on vehicle collaborative queue control, vehicle collaborative decision making, and vehicle collaborative positioning. In the case of taking the infrastructure as the object, this paper expounds the communication security, communication delay, and communication optimization algorithm of the vehicle terminal and the road terminal of intelligent connected vehicles. In the case of taking the test site as the object, this paper expounds the development process and research status of the real vehicle road test platform, virtual test platform, test method, and evaluation mechanism, and analyzes the problems existing in the intelligent connected vehicle test environment. Finally, the future development trend and limitations of intelligent networked vehicle collaborative control system are discussed. This paper summarizes the intelligent connected car collaborative control system, and puts forward the next problems to be solved and the direction of further exploration. The research results can provide a reference for the cooperative driving of intelligent vehicles.

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Development of Sensors Testbed for Autonomous Vehicles

Cited by 3 publications

References 4 publications

A Modulated Approach for Improving MFSK RADARS to Resolve Mutual Interference on Autonomous Vehicles (AVs)

A Modulated Approach for Improving MFSK RADARS to Resolve Mutual Interference on Autonomous Vehicles (AVs)

An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions

A Review of Intelligent Connected Vehicle Cooperative Driving Development

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