Driver Prediction for Automative Industry

From ISLAB/CAISR
Title Driver Prediction for Automative Industry
Summary Investigate if and how it is possible to predict the drivers actions and inentions in a predefined limited number of scenarios
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Prerequisites
Author Stefan Karlsson, Cristofer Englund
Supervisor Stefan Karlsson, Cristofer Englund
Level Master
Status Open

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The goal is to investigate if and how it is possible to predict the drivers actions and inentions in a predefined limited number of scenarios.

This master thesis will conduct experiments on the data from FOT projects. This data is from the vehicles during every-day-use. The current database consists of vehicle Controller Area Network (CAN) data and video material from cameras in the vehicle both facing the road and the driver from 100 cars and 20 trucks.

The project will develop knowledge in driver intention prediction. The scenarios to study will be defined in the beginning of the project and the data will be extracted from the FOT database. Investigations on and evaluation on methods that are suitable will be performed. Based on the experimental investigations a final method will be proposed and the results will be presented.

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