Dependable Autonomous Systems of High Performance, Reliability and Integrity
Commercial applications for the everyday deployment of autonomous systems based on robotic and intelligent systems technologies require the highest levels of performance, reliability and integrity. The general public expects intelligent machines to be fully operational 100% of the time. People expect autonomous technologies to operate at higher levels of performance and safety than people themselves exhibit. For example smart car technologies are expected to cause ZERO accidents while human error kills more 250,000 people on our roads every year! This talk will describe the principles that have been developed over of the last 10 years through exhaustive trial and error testing to underpin autonomous systems that are suitable for real-world deployment. Currently, it is not yet possible to realise an autonomous system that doesn't fail periodically. Even if the mean rate between failures is days or weeks, a single failure could have catastrophic consequences. The approach we have adopted to address this situation has been to build-in monitoring systems that continually check all key system parameters and variables. If the monitored parameters move outside tightly defined bounds the system will safely shutdown, and alert the human supervisor. The failure conditions are logged and then further testing and debugging is performed. The value and appropriateness of our approach will be shown by a number of real-world studies. We will show that how it is possible to design computer vision systems for human-machine applications can operate with over 99% reliability, in all lighting conditions, for all types of users irrespective of age, race or visual appearance. These systems have been used in automotive and sports applications. We have also show how this approach has been used to design field robotic systems that have deployed in automobile safety systems and 24/7 mining applications.
