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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning for Autonomous Vehicle Control: Algorithms, State-Of-The-Art, and Future Prospects

Deep Learning for Autonomous Vehicle Control: Algorithms, State-Of-The-Art, and Future Prospects

Kuutti, Sampo
Fallah, Saber
Bowden, Richard

Paperback

Series: Synthesis Lectures on Advances in Automotive Technology

Technology & Engineering

ISBN10: 3031003748
ISBN13: 9783031003745
Publisher: Springer Nature
Published: Aug 8 2019
Pages: 70
Weight: 0.35
Height: 0.17 Width: 7.50 Depth: 9.25
Language: English

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.

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