Today we are joined by Alexandre Bayen, Director of the Institute for Transportation Studies and Professor at UC Berkeley.
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With a background in control theory, Alexandre found the fields of robotics, machine learning, action optimization and many more all progressively merging and evolving at a rapid pace. This led to his current research in mixed-autonomy traffic to understand how the growing automation in self-driving vehicles can be used to improve mobility and optimization on a scale that greatly impacts the flow of traffic dynamics.
At last year’s AWS re:Invent, Alexandre presented on the future of mixed-autonomy traffic and the two major revolutions he predicts will take place in the next 10-15 years. First, is a model-free world for vehicle planning and coordination, where deep reinforcement learning is predominantly used to learn via simulation. Second, is end-to-end pixel learning and working directly with renderings to gather inputs. Listen to this thought-provoking episode and hear Alexandre share his current research challenges, examples from his vast experience, and where he sees the immediate potential for the future of our transportation systems.
From the Interview
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“More On That Later” by Lee Rosevere licensed under CC By 4.0