کنترل سرعت هوشمند وسایل نقلیه خودمختار
Abstract
I. Introduction
II. Collaborative Framework
III. Smart Longitudinal Velocity Control of AV to Avoid Distracted Driver
IV. Benefit Analysis of Collaborative Framework
V. Simulations
Authors
Figures
References
Abstract
With the development of commercialized autonomous vehicles (AVs), the interaction between AVs and human-driven vehicles has become increasingly important. Nevertheless, on the one hand, complex driver behaviors like distraction are hard to detect by AVs, which may lead to traffic accidents because of the late alert to the following vehicles. On the other hand, advanced techniques such as the real-time image or video processing and vehicle-to-vehicle (V2V) communications make it possible to let AVs receive monitoring signals from nearby vehicles, predict the latent risks, and make smart control to avoid the vehicles driven by distracted drivers. Hence, in this paper, we envisage a collaborative framework integrating human driver distraction monitoring, V2V communications, and AV velocity control. Then, we design the smart velocity control of AVs by taking into consideration the distraction behaviors of the drivers in the human-driven vehicles, and by formulating it as a feasible optimization problem based on model predictive control (MPC) strategies. Furthermore, we analyze the safety benefits that the collaborative framework could help improve on the condition of preserving traffic performance. Finally, we implement the contrast tests of real-time evaluation on driver distraction monitoring based on convolutional neural networks (CNNs) and perform simulations of smart velocity control strategies of the AV at avoiding the distracted driver and reducing rear-end collisions. Through the analysis and the simulations, we show our framework could increase the safety regions, reduce the rear-end collisions, and thus increase the safety of the whole transportation networks.
Introduction
In recent years, intelligent transportation system (ITS) has attracted much attention from both academia and industry, which is expected to improve transportation safety and mobility [1]–[3]. As one of the significant technologies, autonomous driving systems have been aimed to bring us safety, as well as autonomy, namely making driving decisions independently [4]–[6]. Meanwhile, much attention has been paid to the theoretical research and industrial practice of autonomous vehicles (AVs) [7]–[9]. Nevertheless, AVs and human drivers are expected to coexist for a long time. Thus, it is important to consider their interactions [10], [11]. In particular, on the one hand, the driving manner of AVs may seem to be stubborn without face-to-face communications between human drivers. For instance, many AVs only use on-board sensors to perceive the environment thus having difficulties anticipating the motion of surrounding human-driven vehicles [12]. On the other hand, the implicit and complex states and behaviors of human drivers like distractions and fatigue, which are hard to detect by the AVs, may result in sudden brakes and subsequent accidents because of the late alert to the following AVs. Moreover, the movements of human-driven vehicles usually involve a high level of uncertainty and randomness [13], and sometimes human drivers dangerously trade off safety for throughput [14], both bringing potential risks to the AVs.