Motivation for the use of statistical machine learning techniques in the automotive domain arises from our development of context aware intelligent driver assistance systems, specifically, Driver Workload Management systems. Such systems integrate, prioritize, and manage information from the roadway, vehicle, cockpit, driver, infotainment devices, and then deliver it through a multimodal user interface. This could include incoming cell phone calls, email, navigation information, fuel level, and oil pressure to name a very few. In essence, the workload manager attempts to get the right information to the driver at the right time and in the right way in order that driver performance is optimized and distraction is minimized.
In this chapter we describe three major efforts that have employed our machine learning approach. First, we discuss how we have utilized our machine learning approach to detect and classify a wide range of driving maneuvers, and describe a semi-automatic data annotation tool we have created to support our modeling effort. Second, we perform a large scale automotive sensor selection study towards intelligent driver assistance systems. Finally, we turn our attention to creating a system that detects driver inattention by using sensors that are available in the current vehicle fleet (including forwarding looking radar and video-based lane departure system) instead of head and eye tracking systems.
This approach resulted in the creation of two generations of our workload manager system called Driver Advocate, Driver Advocate that was based on data rather than just expert opinions. The described techniques helped reduce the research cycle times while resulting in broader insight. There was rigorous quantification of theoretical sensor subsystem performance limits and optimal subsystem choices given economic price points. The resulting system performance specs and architecture design created a workload manager that had a positive impact on driver performance [23, 33].
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Torkkola, K. et al. (2008). Understanding Driving Activity Using Ensemble Methods. In: Prokhorov, D. (eds) Computational Intelligence in Automotive Applications. Studies in Computational Intelligence, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79257-4_3
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