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Towards developing of advanced driver specific active/passive safety systems it is important to be able to continuously evaluate driving performance variations. These variations are best captured when evaluated against similar driving patterns or maneuvers. Hence, accurate maneuver recognition in the preliminary stage is vital for the evaluation of driving performance. Rather than using simulated or fixed test track data, it is important to collect and analyze on-road real-traffic naturalistic driving data to account for all possible driving variations in different maneuvers. Towards this, massive free style naturalistic driving data corpora are being collected. Human transcription of these massive corpora is not only a tedious task, but also subjective and hence prone to errors/inconsistencies which can be due to multiple transcribers as well as lack of enough training/instructions.

The use of portable devices for in-vehicle environments has become a major cause for driver distraction which can be a contributing factor in crashes of varying intensity. Despite this fact, the number of drivers choosing to use using these devices while driving is increasing rapidly. On the positive side, smart portable devices are equipped with a variety of useful sensors such as cameras, microphones, accelerometer, gyroscope, etc. which could be leveraged to help reduce driver distraction. Careful utilization and delivery of information extracted from these sensors could potentially prove more useful to drivers rather than distracting them. As a proof of concept, using the sensor information available from an off-the-shelf smart portable device, an automatic system is proposed here for driving maneuver recognition and analysis. Driving maneuvers form the basic building blocks of the driver's intent in completing a route.