Development of Deep Learning Based Human-Centered Threat Assessment for Application to Automated Driving Vehicle

Abstract

This paper describes the development of deep learning based human-centered threat assessment for application to automated driving vehicle. To achieve naturalistic driver model that would feel natural while safe to a human driver, manual driving characteristics are investigated through real-world driving test data. A probabilistic threat assessment with predicted collision time and collision probability is conducted to evaluate driving situations. On the basis of collision risk analysis, two kinds of deep learning have been implemented to reflect human driving characteristics for automated driving. A deep neural network (DNN) and recurrent neural network (RNN) are designed by neural architecture search (NAS), and by learning from the sequential data, respectively. The NAS is used to automatically design the individual driver’s neural network for efficient and effortless design process while ensuring training performance. Sequential trends in the host vehicle’s state can be incorporated through hand-made RNN. It has been shown from human-centered risk assessment simulations that two successfully designed deep learning driver models can provide conservative and progressive driving behavior similar to a manual human driver in both acceleration and deceleration situations by preventing collision.

A Novel Gated Recurrent Unit Network Based on SVM and Moth-Flame Optimization Algorithm for Behavior Decision-Making of Autonomous Vehicles

“…where  is the standard gamma function. According to (18) and (20), the position updating method of moths  …”

“…Therefore, Recurrent Neural Network (RNN) methods, which have advantages in learning the nonlinear characteristics of time series, are widely used [19]. Shin et al [20] used RNN to learn from the sequential data to predict collision probability. Zou et al [21] put the continuous features extracted by CNN as inputs of RNN and obtained the prediction of lane selection.…”

Artificial Intelligence Applications to Smart City and Smart Enterprise

“…Shin et al [10] implemented two kinds of deep learning techniques to reflect human driving behavior for automated car driving. A deep neural network (DNN) and a recurrent neural network (RNN) were designed by neural architecture search (NAS).…”

An End-to-End Learning-Based Row-Following System for an Agricultural Robot in Structured Apple Orchards

“…Most of the current research on self-driving (autonomous) systems using deep learning mainly focuses on landto-road navigation [26][27][28][29][30][31][32][33], while outdoor robot navigation studies remain relatively low. Muller et al developed a visual-based obstacle avoidance system using deep networks for an outdoor robot.…”