Learning a deep neural net policy for end-to-end control of autonomous vehicles

By Viktor Rausch, Andreas Hansen, Eugen Solowjow, Chang Liu, Edwin J. Kreuzer, J. Karl Hedrick. 2017

120 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Agile Autonomous Driving using End-to-End Deep Imitation Learning (2017, background): “Note this control frequency is significantly higher than [2] (10 Hz), [24] (12 Hz), and [19] (15 Hz).”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “However, most of these existing works [229]–[233] focus only on issues of autonomy and do not consider the challenges of wireless communications.”
Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning (2017, background): “Note this control frequency is signiﬁcantly higher than [7] (10 Hz), [9] (12 Hz), and [10] (15 Hz).”
Credit Card Fraud Detection using Machine Learning Algorithms (2019, unknown): “(Rausch et al., 2017) Rausch, V., Hansen, A., Solowjow, E., Liu, C., Kreuzer, E., and Hedrick, J. K. (2017 (Segota et al., 2021) Šegota, S. B., Anđelić, N., Mrzljak, V., Lorencin, I., Kurić, I., and Car, Z. (2021).”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “er, the contents that have been stored, and the route. Based on this predicted information, each vehicle can select the optimal target vehicles for content transmission. The existing literature [229]–[233] has studied a number of problems pertaining to the use of ANNs for autonomous vehicles, such a”
Incremental Lifelong Deep Learning for Autonomous Vehicles (2018, background): “to learn action policies and perform driving behaviors related to steering, throttle, and braking actuators [2] [3].”
A survey of deep learning techniques for autonomous driving (2019, methodology): “uge amounts of training samples (see Section 8). End2End control papers mainly employ either deep neural networks trained ofﬂine on real-world and/or synthetic data [119], [113], [114], [115], [120], [116], [117], [121], [118], or Deep Reinforcement Learning (DRL) systems trained and evaluated in si”