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In recent years, the development of advanced driving assistance systems (ADAS) has grown significantly within the transportation industry to assist drivers for making safe maneuvers. A major component in developing these assistance systems are driving behavior prediction and recognition models. These models aim to infer driving behaviors based on different sources and parameters using complex mathematical models. Machine learning algorithms are being used increasingly to develop these models. In this contribution, two formerly developed trainable models, which are an improved Hidden Markov Model (HMM) and a state machine model, are combined for the recognition of three lane changing behaviors (lane change to the right (LCR), lane keeping (LK), and lane change to the left (LCL). In the improved HMM, a prefilter is implemented on two sets of observation variables (input variables of HMM): one consisting of distances and velocity deviation, while the other consists of time to collision (TTC) variables. To develop an optimal model, thresholds of the prefilter are optimized using a Non-Dominated Sorting Genetic-Algorithm-II. The aim is to investigate if the proposed model is able to produce estimations with high accuracy (ACC), detection rates (DR), and low false alarm rates (FAR). In addition, the performance based on applying the prefilter on the two sets of variables are compared. Comparisons to an individual improved HMM and an ANN-based state machine approach are also addressed. The obtained results show that the application of prefilter on the TTC variables improves the estimation performance. Furthermore, the proposed approach outperforms other approaches.

A Study on a HMM-Based State Machine Approach for Lane Changing Behavior Recognition