Evaluation of a semi-autonomous lane departure correction system using naturalistic driving data

Abstract

Abstract-Evaluating the effectiveness and benefits of driver assistance systems is essential for improving the system performance. In this paper, we propose an efficient evaluation method for a semi-autonomous lane departure correction system. To achieve this, we apply a bounded Gaussian mixture model to describe drivers' stochastic lane departure behavior learned from naturalistic driving data, which can regenerate departure behaviors to evaluate the lane departure correction system. In the stochastic lane departure model, we conduct a dimension reduction to reduce the computation cost. Finally, to show the advantages of our proposed evaluation approach, we compare steering systems with and without lane departure assistance based on the stochastic lane departure model. The simulation results show that the proposed method can effectively evaluate the lane departure correction system.

Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications

“…Autonomous vehicles play a vitally important role in intelligent traffic systems, road safety, and driving workload reduction [1]. A lot of automated vehicle research has focused on how to learn end-to-end controllers [2], how to design and generate traffic scenarios for automated vehicle evaluation [3], how to understand the traffic scenes using naturalistic driving data based on deep learning and machine learning techniques, capable of offering supportive interventions to human drivers during a specific task. With this purpose, the automated vehicles need to fully understand how driving scenarios are changing and correctly predict what other road users surrounded will do.…”

Data Collection and Processing Methods for the Evaluation of Vehicle Road Departure Detection Systems

“…Authors in [7] proposed a system based on a closed-loop driver decision estimator (DDE), which determines the risk of road departure. Some other related research works include traffic forecast using deep learning method [8], evaluation of lane departure correction system (LDCS) based on the stochastic driver model [9], analysis of the LDCS utilizing naturalistic driving data [10], and so on.…”