Importance of Synchronizing Radars with V2X communication for Radar Interference Mitigation

Roudiere, Sylvain; Martinez, Vincent; Delahaye, Daniel

doi:10.1109/itsc48978.2021.9565081

Cited by 2 publications

(5 citation statements)

References 2 publications

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“…These results indicate that to avoid interference while not changing radar parameters to often, it is a good idea to share the available radar band in a way that takes into account the orientation of the radars (or the traffic). Mitigation strategies splitting the available band into two distinct parts depending on the radar orientation have already been simulated in [ 15 , 16 ] and yielded good interference mitigation, but the optimal radar band sharing method seems to require a less strict separation to allow two radars facing in the opposite direction to still use the same waveform, as shown by the non-zero distribution of the picture on the left.…”

Section: Resultsmentioning

confidence: 99%

“…Our previous works [ 15 , 32 ] focused on V2X-based interference mitigation strategies with a Python simulator, which was built to generate realistic traffic scenarios including radars. This framework uses the recognized and widely used SUMO software (SUMO is an open-source continuous multi-modal and highly portable simulation package made with Eclipse.…”

Section: Simulation Environmentmentioning

confidence: 99%

“…However, Figure 1 and Figure 2 are provided to exemplify possible organizations of the radar band. Figure 1 is an example of a very constrained band split used in our previous work [ 16 ], but a simple approach to radar band organization could be a grid-like split as illustrated in Figure 2 . The number of available resources depends on the shape of these time-frequency zones.…”

Section: Simulation Environmentmentioning

confidence: 99%

“…Conversely, our previous work on the subject [ 15 ], and ref. [ 16 ] focused on avoiding interference using a communication channel such as V2X (vehicle to everything).…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, our previous work on the subject [ 15 ], and ref. [ 16 ] focused on avoiding interference using a communication channel such as V2X (vehicle to everything). V2X usually defines an ad hoc network in the 5.9 GHz ITS (intelligent transport services) band, and regional implementations (e.g., ETSI ITS-G5 in Europe) can be based either on the IEEE 802.11p/bd Wi-Fi standard, or on the 3GPP standards such as 4G LTE-V2X and 5G NR V2X [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars

Roudiere¹,

Martinez²,

Maréchal

et al. 2023

Sensors

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The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

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Section: Resultsmentioning

confidence: 99%

Section: Simulation Environmentmentioning

confidence: 99%

Section: Simulation Environmentmentioning

confidence: 99%

“…Conversely, our previous work on the subject [ 15 ], and ref. [ 16 ] focused on avoiding interference using a communication channel such as V2X (vehicle to everything).…”

Section: Introductionmentioning

confidence: 99%