Panel 4-Cognitive vision, robotics and autonomy
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The document discusses autonomous vehicle research at the Surrey Autonomous Testbed. It provides details on the testbed vehicle, sensors used including cameras and lidar, and supporting research projects. The testbed aims to build and demonstrate a fully autonomous vehicle capable of parking and summon functions for under 贈15K. It also briefly outlines different waves of artificial intelligence including handcrafted knowledge, statistical learning, and contextual adaptation as it relates to autonomous driving work.
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Panel 4-Cognitive vision, robotics and autonomy
- 1. C-Lab Cognitive Vision, Robotics and Autonomy Monday, 20 January 2020 1 Prof R Bowden Centre for Vision Speech and Signal Processing
- 2. CVSSP Centre for Vision, Speech and Signal Processing Creating machines that can see, hear and understand the world around them
- 4. Surrey Autonomous Testbed The testbed is very similar to the street drone, using the same Twizy vehicle, sensors and processing platform Twizy Cargo Nvidia PX2 Drive 6 Sekonix cameras (street drone as 8 but same coverage, less overlap) Ouster 64 beam lidar Wheel odometry, not available on street drone Supports wider research and collaborative projects with Parkopedia, JLR, McLarren, FLIR amongst others Provides Surrey with a data capture and demonstration platform
- 5. Surrey Autonomous Testbed Two phase build: Phase 1 sensor recording and onboard processing Phase 2 actuators to provide full control Entire build opensource: http://autonomous.home.blog How to build an autonomous vehicle for < 贈15K
- 7. Demonstration scenario 1. Parking process is started via smartphone 2. Vehicle enters a car park, localises itself using indoor maps and navigates to a space 3. Driver summons parked vehicle back via smartphone 4. Vehicle exits a car park and is picked by a driver in a pick up zone
- 8. Work related to Autonomous Driving Automatic extrinsic calibration of car mounted sensors Reinforcement Learning for vehicle control and safety verification Feature Learning across lighting and weather Vehicle state estimation, sensor fusion and side slip estimation Semantic Segmentation Monoscopic depth estimation and scene estimation
- 9. DARPA 3 Waves of AI Wave 1 Handcrafted Knowledge e.g. classical AI, chess computers Engineers create sets of rules to represent knowledge in welldefined domains The structure of the knowledge is defined by humans The specifics are explored by the machine Enables reasoning over narrowly defined problems No learning capability and poor handling of uncertainty Wave 2 Statistical Learning e.g. machine and deep learning, alpha go Engineers create statistical models for specific problem domains and train them on big data Nuanced classification and prediction capabilities No contextual capability and minimal reasoning ability Statistically impressive, but individually unreliable Wave 3 - Contextual adaptation Systems construct contextual explanatory models for classes of real world phenomena