Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model

By Paul Drews, Grady Williams, Brian Goldfain, Evangelos A. Theodorou, James M. Rehg. 2017

44 citations recorded by the source corpus.

We present a framework for vision-based model predictive control (MPC) for the task of aggressive, high-speed autonomous driving. Our approach uses deep convolutional neural networks to predict cost functions from input video which are directly suitable for online trajectory optimization with MPC. We demonstrate the method in a high speed autonomous driving scenario, where we use a single monocular camera and a deep convolutional neural network to predict a cost map of the track in front of the vehicle. Results are demonstrated on a 1:5 scale autonomous vehicle given the task of high speed, aggressive driving.

Subfields: 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, methodology): “While the requirement on GPS and IMU is relaxed by using a vision-based cost map in [5], a large dataset (300,000 images) was used to train the model, expensive onthe-fly planning is still required, and speed performance is compromised.”
Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning (2017, methodology): “Our task is most similar to the task considered by Williams et al. [2], [3] and Drews et al. [14].”
A Survey of End-to-End Driving: Architectures and Training Methods (2020, background): “It has been proposed to output cost maps, which contain information about where it is safe to drive [10], [108].”
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing (2022, methodology): “The method of DNNs has become more and more popular in the recent years and different authors proposed so called end-to-end approaches that solve the autonomous driving task.”
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020, background): “A lot of impressive applications of CNN are emerging in modern society, from simple cat–dog classifiers [190] to sophisticated autonomous vehicles [189], [191], [192].”
A survey of deep learning techniques for autonomous driving (2019, methodology): “), (Panomruttanarug, 2017), (Kapania and Gerdes, 2015), (Yang et al., 2017b) and Model Predictive Control (MPC) (Lefvre et al., 2016) (Lefevre et al., 2015), (Ostafew et al., 2015), (Ostafew, 2016), (Drews et al., 2017a), (Drews et al., 2017b), (Rosolia et al., 2017), (Pan et al., 2017), (Pan et al.”