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

2017/07/17 by Paul Drews, Drews, Paul, Grady Williams +7 · 1 citation
Computer Science · Engineering · #68T40 #Advanced Neural Network Applications #Advanced Vision and Imaging #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.1707.05303

openalex publication_date 2017/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Adam: A Method for Stochastic Optimization
• End to End Learning for Self-Driving Cars
• DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Cited by

• Interpretable Decision-Making for End-to-End Autonomous Driving
• Fundamental tenis : resep meraih kemenangan / Tony Mottram

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