Kuutti Sampo, Fallah Saber, Bowden Richard Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects 9781681736075

Варианты приобретения

Цена: 6237.00р.
Кол-во:
о цене
Наличие: Отсутствует.
Возможна поставка под заказ. Дата поступления на склад уточняется после оформления заказа

Добавить в корзину

в Мои желания

Автор: Kuutti Sampo, Fallah Saber, Bowden Richard
Название: Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects
ISBN: 9781681736075
Издательство: Mare Nostrum (Eurospan)
Классификация:

Электроника

Техника автоматического регулирования

Автомобильный транспорт: техника, перевозки и профессии

Искусственный интеллект

ISBN-10: 1681736071
Обложка/Формат: Paperback
Страницы: 80
Вес: 0.16 кг.
Дата издания: 30.10.2019
Серия: Engineering
Язык: English
Размер: 235 x 191 x 4
Читательская аудитория: Professional and scholarly
Ключевые слова: Artificial intelligence,Automatic control engineering,Automotive technology & trades, TECHNOLOGY & ENGINEERING / Automotive,TECHNOLOGY & ENGINEERING / Automation,COMPUTERS / Intelligence (AI) & Semantics
Подзаголовок: Algorithms, state-of-the-art, and future prospects
Рейтинг:
Поставляется из: Англии
Описание: The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.

Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects, Kuutti Sampo, Fallah Saber, Bowden Richard