{"id":33341,"date":"2023-01-19T16:58:51","date_gmt":"2023-01-19T15:58:51","guid":{"rendered":"https:\/\/cesam.community\/?p=33341"},"modified":"2023-02-09T17:31:27","modified_gmt":"2023-02-09T16:31:27","slug":"a-multiobjective-systems-architecture-model-for-sensor-selection-in-autonomous-vehicle-navigation-2","status":"publish","type":"post","link":"https:\/\/cesam.community\/fr\/2023\/01\/19\/a-multiobjective-systems-architecture-model-for-sensor-selection-in-autonomous-vehicle-navigation-2\/","title":{"rendered":"A Multiobjective Systems Architecture Model for Sensor Selection in Autonomous Vehicle Navigation"},"content":{"rendered":"

A Multiobjective Systems Architecture Model for Sensor Selection in Autonomous Vehicle Navigation<\/h1>

Speaker:
\n<\/strong>Anne Collin | MIT
\n<\/strong><\/p>\n

Abstract:
\n<\/strong>Understanding and quantifying the performance of sensing architectures on autonomous vehicles is a necessary step towards certification. However, once this evaluation can be performed, the combinatorial number of potential sensors on the vehicle limits the efficiency of a design tradespace exploration. Several figures of merit emerge when choosing a sensor suite; its performance for a specific autonomy task, its monetary cost, energy consumption, and contribution to the latency of the entire system.<\/p>\n

In this paper, we present formulations to evaluate a sensor combination across these dimensions for the localization and mapping task, as well as a method to enumerate architectures around the Pareto Front efficiently. We find that, on a benchmarked environment for this task, combinations with LiDARs are situated on the Pareto Front.<\/p>\n<\/div><\/div><\/div><\/div><\/section>

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To scroll through the slides use the toolbar at the bottom of the presentation.<\/em><\/span><\/p>\n

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<\/div>A Multiobjective Systems Architecture Model for Sensor Selection in Autonomous Vehicle Navigation<\/a>
In this paper, we present formulations to evaluate a sensor combination across these dimensions for...<\/div>
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In this paper, we present formulations to evaluate a sensor combination across these dimensions for the localization and mapping task, as well as a method to enumerate architectures around the Pareto Front efficiently. We find that, on a benchmarked environment for this task, combinations with LiDARs are situated on the Pareto Front…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[231,225],"tags":[181],"yoast_head":"\nCSD&M: Multiobjective Systems Architecture Model for Sensor Selection<\/title>\n<meta name=\"description\" content=\"Formulations to evaluate a sensor combination across these dimensions for the localization and mapping task\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cesam.community\/fr\/2023\/01\/19\/a-multiobjective-systems-architecture-model-for-sensor-selection-in-autonomous-vehicle-navigation-2\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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