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Predictions of driver's intentions and their behaviors using the road is ofgreat importance for planning and decision making processes of autonomousdriving vehicles. In particular, relatively short-term driving intentions arethe fundamental units that constitute more sophisticated driving goals,behaviors, such as overtaking the slow vehicle in front, exit or merge onto ahigh way, etc. While it is not uncommon that most of the time human driver canrationalize, in advance, various on-road behaviors, intentions, as well as theassociated risks, aggressiveness, reciprocity characteristics, etc., suchreasoning skills can be challenging and difficult for an autonomous drivingsystem to learn. In this article, we demonstrate a disciplined methodology thatcan be used to build and train a predictive drive system, therefore to learnthe on-road characteristics aforementioned.

Predictions of short-term driving intention using recurrent neural network on sequential data