Enabling Computer-vision-based Autonomous Driving with Affordable and Reliable sensors

Speaker:  Shaoshan Liu – Fremont, CA, United States
Topic(s):  Architecture, Embedded Systems and Electronics, Robotics

Abstract

Autonomous vehicles, like humans, need good eyes and a good brain to drive safely. Traditionally, LiDAR is the main sensor in autonomous driving, and it is the critical piece in both localization and obstacle-recognition scenarios. However, LiDAR has several major drawbacks, including extremely high cost, lack of, inconsistency in changing weather conditions, etc. As a result, PerceptIn investigated whether cars could drive themselves with computer vision.
 
The argument against this concept is that the camera does not provide accurate localization or a good obstacle-detection mechanism, especially when the object is far away. But do we actually need centimeter-accurate localization all the time? RTK and PPP GPS already provide centimeter-accurate localization, and if humans can drive cars with meter-accurate GPS, we should be able to do the same with driverless cars. If this is achievable, high-definition maps may not be needed for localization. Google Maps and Google Street View may suffice—a leap forward in autonomous driving development. And a combination of stereo vision, sonar, and millimeter radar could be to achieve high-fidelity obstacle avoidance.

Shaoshan Liu explains how PerceptIn designed and implemented its high-definition, stereo 360-degree camera sensors targeted for computer-vision-based autonomous driving. This sensor has an effective range of over 30 meters with no blind spots and can be use for obstacle detection as well as localization. Shaoshan discusses the sensor as well as the obstacle detection algorithm and the localization algorithm that come with this hardware.

About this Lecture

Number of Slides:  40
Duration:  45 minutes
Languages Available:  English
Last Updated: 

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