AUDIO: When AI learns bad habits… and other AV challenges


One of the first lessons we learned about autonomous travel remains true today: Building a self-driving car is a lot more difficult than many people expected. In this clip from the Thinking Transportation podcast Bob Brydia, senior research scientist at the Texas A&M Transportation Institute, muses on the possibilities of machine learning systems learning the wrong thing. Listen to the whole episode (in which he discusses further the progress made in the past year related to self-driving vehicles becoming commonplace on our roadways, and how far they have yet to go) and more like it at the Thinking Transportation podcast homepage, or find it on your favorite streaming service.

TTI Senior Research Scientist Bob Brydia is a transportation technologist with more than 30 years of diverse experience with national, state and local transportation research sponsors. Bob has led several large-scale deployment projects, including testing automated vehicles in real-world environments, and has developed multiple innovations to advance transportation.

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About Author


Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).