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.
Highlights
Autonomous vehicles are designed to take human error out of driving actions, which should help make self-driving vehicles safer, dramatically reducing the number of road accidents
Engine RPM, turbine speed of the torque converter, throttle position, and gear status are obtained from the engine control unit (ECU) and each sensor through the position, and gear status are obtained from the engine control unit (ECU) and each sensor through controller area network (CAN)
The neural architecture search (NAS) method can design individual deep neural network (DNN) architecture automatically according to every single driver dataset
Summary
Autonomous vehicles are designed to take human error out of driving actions, which should help make self-driving vehicles safer, dramatically reducing the number of road accidents. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand [10] For these reasons, various studies have been carried out to generate the network architecture automatically [11]. Structure learning is a very useful instrument that is able to automatically find an appropriate artificial neural network (ANN) architecture [12] For this reason, the structure learning algorithm is used to generate the topology of ANN in this study. In order to express the sequential data in both acceleration and deceleration situations of human driving, hand-made RNN is implemented in this paper.
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