Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control

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Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing (2018, background): “A different line of work combines network predictions with model predictive control by regressing the cost function from a single image [2].”
Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018, background): “While being independent of any global map and position estimate, these methods are not directly applicable to our specific problem due to their high computational complexity [27, 11], their low maximum speed [13] or the inherent difficulties of generalizing to 3D motions [25, 4, 24].”
A survey of deep learning techniques for autonomous driving (2019, background): “, 2016), driving dynamics for race cars operating at their handling limits (Drews et al., 2017; Rosolia et al., 2017), as well as to improve path tracking accuracy (Brunner, Rosolia, Gonzales, & Borrelli, 2017; Ostafew et al.”
Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, methodology): “Drews et al. [11] learn a cost function from monocular images which is used with Model Predictive Control (MPC) for aggressive driving on a race track.”