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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
H2020Обмен на изследователи2018–2024

ULTRACEPT · Ultra-layered perception with brain-inspired information processing for vehicle collision avoidance

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2018-12-01 → 2024-09-30
Финансиране от ЕС
1 894 500 €
Участници
19
Схема
MSCA-RISE

Линиите свързват координатора с партньорите.

Накратко на български

Системи за откриване на сблъсъци при автономни автомобили, вдъхновени от начина, по който мозъкът обработва информация. Това помага за създаването на по-надеждни и евтини сензори, които да работят при дъжд, мъгла или в градска среда, за да се спасят човешки животи.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Ultra-layered perception with brain-inspired information processing for vehicle collision avoidance

Although in their early stages, autonomous vehicles, have demonstrated huge potential in shaping future lifestyles. However, to be accepted by ordinary users, autonomous vehicles have a critical issue to solve – being trustworthy at collision detection. Autonomous vehicles that experience accidents once every few months or years would be unacceptable to the general public. In the real world, human driven vehicles collide at every second. More than 1.3 million people are killed by road accidents every single year. The current approaches for vehicle collision detection such as vehicle to vehicle communication, radar, laser-based Lidar, and GPS are far from acceptable in terms of reliability, cost, energy consumption, and size. For example, radar is too sensitive to metallic material, Lidar is too expensive and does not work well on absorbing/reflective surfaces, GPS based methods are difficult in cities with tall buildings, vehicle to vehicle communication cannot detect pedestrians or any objects unconnected, segmentation based vision methods are too computing power-thirsty to be miniaturized, and normal vision sensors cannot cope with fog, rain and dim environment at night. To save people’s lives and make autonomous vehicles safe to serve human society, a new type of trustworthy, robust, low-cost, and low energy consumption vehicle collision detection and avoidance systems are needed. This ULTRACEPT consortium proposes an innovative solution with brain-inspired multiple layered and multiple modalities information processing for trustworthy vehicle collision detection. Connecting multidisciplinary teams from different countries together via staff exchange and collaboration, it takes the advantages of low-cost spatial-temporal and parallel computing capacity of bio-inspired visual neural systems and multiple modalities data inputs in extracting potential collision cues at complex weather and lighting conditions.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Autonomous vehicles, although in its early stage, have demonstrated huge potential in shaping future life styles to many of us. However, to be accepted by ordinary users, autonomous vehicles have a critical issue to solve – this is trustworthy collision detection. No one likes an autonomous car that is doomed to a collision accident once every few years or months. In the real world, collision does happen at every second - more than 1.3 million people are killed by road accidents every single year. The current approaches for vehicle collision detection such as vehicle to vehicle communication, radar, laser based Lidar and GPS are far from acceptable in terms of reliability, cost, energy consumption and size. For example, radar is too sensitive to metallic material, Lidar is too expensive and it does not work well on absorbing/reflective surfaces, GPS based methods are difficult in cities with high buildings, vehicle to vehicle communication cannot detect pedestrians or any objects unconnected, segmentation based vision methods are too computing power thirsty to be miniaturized, and normal vision sensors cannot cope with fog, rain and dim environment at night. To save people’s lives and to make autonomous vehicles safer to serve human society, a new type of trustworthy, robust, low cost, and low energy consumption vehicle collision detection and avoidance systems are badly needed.This consortium proposes an innovative solution with brain-inspired multiple layered and multiple modalities information processing for trustworthy vehicle collision detection. It takes the advantages of low cost spatial-temporal and parallel computing capacity of bio-inspired visual neural systems and multiple modalities data inputs in extracting potential collision cues at complex weather and lighting conditions.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз