AI-Enabled Autonomous Driving: Enhancing Safety and Efficiency through Predictive Analytics
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Autonomous driving (AD) is an emerging technology promising to revolutionize the future of transportation. Apart from offering an opportunity for improved road safety through the reduction of human errors, the application of AD will enhance traffic efficiency by allowing for improved driving and traffic flow stability, as advanced algorithms for predictive analytics may be developed.
In this paper, we put our emphasis on the fact that the dynamics of an automated vehicle (AV) interacting with human drivers is weakly collective open-system complex, intrinsically temporal, and representation-hierarchical. To target the realization challenge of AI-enabled autonomy driving, we developed predictive planning with perceptual and learning modules to perform task-relevant scene understanding in operational and tactical planning.
The talk about AI-enabled transportation separates the functional and realization levels ably and links them together in system engineering. The dynamics visualization framework for AI-enabled AD systems is readily expanded to other similar systems and processes in extensive complex systems.
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