Simulator Design for Interference Analysis in Complex Automotive Multi-User Traffic Scenarios

Torres, Lizette Lorraine Tovar; Roos, Fabian; Waldschmidt, Christian

doi:10.1109/radarconf2043947.2020.9266318

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The simulations are performed employing the fast interference simulator presented in [9]. The simulator is a flexible tool capable of representing complex dynamic traffic scenarios involving several vehicles with multiple potential interferer radars.…”

Section: Simulation Resultsmentioning

confidence: 99%

Analysis of Automotive Radar Interference in Complex Traffic Scenarios using Graph Theory

Torres

Waldschmidt

2022

2022 23rd International Radar Symposium (IRS)

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The increasing amount of automotive radar sensors leads to a higher risk of undesired interferences. Complex traffic scenarios comprise several vehicles, each one equipped with multiple sensors. As a result, a significant number of timevariable interference links are present in the scenario. Making use of the graph coloring theory, this paper analyzes a highway and an intersection scenario and gives boundaries regarding the minimum number of resources needed to completely suppress the existent interference links. Based on these results, the scenarios are simulated using the current unregulated scheme and considering a wise distribution of the sensor parameters. It is shown that even in such complex scenarios with thousands of interference links, it is possible to significantly reduce the interference if the available sensor resources are wisely assigned.

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Section: Simulation Resultsmentioning

confidence: 99%

Analysis of Automotive Radar Interference in Complex Traffic Scenarios using Graph Theory

Torres

Waldschmidt

2022

2022 23rd International Radar Symposium (IRS)

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“…The most common method is to treat the target as multiple point scattering centers. For instance, Jiao Guo, Lizette Lorraine et al selected characteristic locations on the target as scatter centers, thereafter calculating the radar’s echo signals using the target’s parameters and radar detection equations [ 51 , 52 ]. To improve the representativeness of scatter centers, Rajan Bhalla and Karin Schuler independently applied the bouncing ray method and the greedy algorithm to choose the most crucial locations on the target as scatter centers [ 53 , 54 ].…”

Section: Radar Modeling Methods For Autonomous Drivingmentioning

confidence: 99%

“…Regarding the influence of weather and ground, Jiao Guo, Zora Slavik incorporated weather factors like rain attenuation into the echo model [ 51 , 56 ]. Lizette Lorraine and colleagues considered the impact of rough ground scattering on echo signals [ 52 ]. Xin Li has extensively studied the distribution characteristics of ground clutter, rain, snow, fog, and other weather clutter in different scenarios and fitted weather clutter and ground clutter under various conditions based on Gaussian distributions [ 57 ].…”

Section: Radar Modeling Methods For Autonomous Drivingmentioning

confidence: 99%

“…Object-level radar models produce outputs that encompass the detectability and parameters of the object. In existing studies, Preeti S. Pillai, Zoltan Ferenc Magosi, Ivan Kravchenko, Jiao Guo, Lizette Lorraine, and Jithin Kannanthara have all incorporated CFAR and clustering modules into their signal-level models to extract lists of targets [ 51 , 52 , 55 , 81 , 82 ].…”

Section: Radar Modeling Methods For Autonomous Drivingmentioning

confidence: 99%

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An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Huang,

Ding,

Deng

2024

Sensors

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Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.

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“…The transmitter and receiver can exchange the information after assuring the synchronization procedure. However, a typical automotive environment includes many radar sensors that can work within the same bandwidth and time duration, leading to interference between the signals and affecting the radar performance [45]. Since the proposed communication method in [29] carries the data partially (e.g., on 10% of the radar bandwidth B), the probability of interference over the communication section can be limited.…”

Section: Multi-user Scenario With Mutual Interferencementioning

confidence: 99%

Preamble-based Synchronization for Communication-assisted Chirp Sequence Radar

Alabd¹,

Nuß²,

Oliveira³

et al. 2022

PIER C

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Chirp sequence has been adopted in automotive applications for its simple generation and flexible integration within radar-centric systems. Besides, recent studies have shown its ability to carry data between communicating vehicles in the surroundings. Since the parameters adopted from current automotive radar sensors can differ at the transmitter side dependent on the automotive supplier, the carrier alignment of the communication receiver of one of the communicated nodes might not concur with the one in the transmitter. This paper presents a novel two-stage synchronization method for communication-assisted chirp sequence (CaCS) signals. The proposed synchronization method applies a sequence of up-and down-chirp as a preamble to estimate frequency and time offsets during the transmission. The suggested synchronization scheme supports partial chirp modulation systems and can be adapted for similar radar-centric systems that employ chirp modulation. The former stage performs a coarse synchronization, reallocates the receive carrier frequency, and corrects eventual time offsets between the communication receiver from one CaCS-node and the transmitter of another node. The carrier allocation at the communication receiver side is based on a combination of spectrum sensing via short-time Fourier transforms and image processing to estimate the transmitting signal pattern (slope, frequency offset, and delay). The latter stage, in its turn, relies on range-Doppler estimation to perform a fine correction of time and frequency offsets and compensates residual offsets of the coarse synchronization stage. Furthermore, the paper analyzes the case of a multi-user scenario with mutual interference between the signals that affects the synchronization and communication data detection. Besides, measurements are provided based on two completely unsynchronized software-defined radios to validate the proposed method. The study also illustrates the influence of the signal-to-noise ratio on the proposed method and verifies it with simulations in MATLAB. As a result, the offsets at the investigated CaCS-node are returned to recover the transmitted data correctly.

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Simulator Design for Interference Analysis in Complex Automotive Multi-User Traffic Scenarios

Cited by 8 publications

References 7 publications

Analysis of Automotive Radar Interference in Complex Traffic Scenarios using Graph Theory

Analysis of Automotive Radar Interference in Complex Traffic Scenarios using Graph Theory

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Preamble-based Synchronization for Communication-assisted Chirp Sequence Radar

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