In this letter, we present a framework for combining deep learning-based road detection, particle filters, and model predictive control (MPC) to drive aggressively using only a monocular camera, IMU, and wheel speed sensors. This framework uses deep convolutional neural networks combined with LSTMs to learn a local cost map representation of the track in front of the vehicle. A particle filter uses this dynamic observation model to localize in a schematic map, and MPC is used to drive aggressively using this particle filter based state estimate. We show extensive real world testing results and demonstrate reliable operation of the vehicle at the friction limits on a complex dirt track. We reach speeds above 27 m/h (12 m/s) on a dirt track with a 105 ft (32 m) long straight using our 1:5 scale test vehicle. 2377-3766
Drews et al. REF use a mono camera processed using CNNs combined with LSTMs to obtain a representation of the track.
Vision-Based High-Speed Driving With a Deep Dynamic Observer
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters
Computer Science Engineering