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The combination of Device-to-Device (D2D) Communication in 5G Cellular Networks and vehicular networks will not only increase the performance of vehicular networks, but also increase the revenues for network operators and services providers. This paper proposes a 5G D2D routing method oriented to vehicular networks, which can increase the connectivity and scalability of vehicular networks while alleviating...
In this paper, driver intention estimation near a road intersection is presented, using discrete hidden Markov models (HMM) and the Hybrid State System (HSS) framework as basis. The development of Advanced Driver Assistance Systems (ADAS) has assisted drivers in many driving scenarios and resulted in safe driving. Developing techniques to estimate driver's intention leads to the advancement of ADAS...
In this paper, the problem of developing a model for signal control system with transit priority using Colored Petri Nets (CPNs) is considered. In a regular four phases signal lights control model, transit detection and two kinds of transit priority strategies are integrated to obtain Colored Petri Nets based transit priority signal control model. The resulting model ensures that transit can pass...
This paper is on a connected and autonomous vehicle hardware-in-the-loop (HiL) simulator for developing automated driving algorithms. This simulator allows the user to run highly realistic hardware-in-the-loop simulation of connected and autonomous driving functions. The HiL simulator of this paper consists of a dSPACE Scalexio system which runs Carsim Real Time with Traffic and Sensors and is connected...
Trafïîc congestion is a serious global problem. Enhanced or deep Q learning algorithms were applied to a traffic simulation study. The enhanced Q learning was based on a repeated local search algorithm, and it was able to find optimal pathways under a multi-agent system. Deep Q learning was also capable of learning a suitable strategy, considering dynamic changes in traffic circumstances at a highway...
Traditional vehicular routing protocols cannot accurately foresee future location of each vehicle for efficient packet forwarding. Recently, the data mining approach has been applied to analyze huge vehicle trajectory data. In this paper, we propose a novel trajectory-based routing (NTR) protocol to improve the packet replication efficiency of vehicles in the Vehicular Delay Tolerant Network (VDTN)...
The interaction between a human driver and an automated driving system may improve when the automation is designed in such a way that it behaves in a human-like manner. This paper introduces a human-like steering model, in which the driver adapts to the risk due to uncertainty in the environment. Current steering models take a risk-neutral approach, while the fields of economics and sensorimotor control...
In this paper we are presenting a novel approach for the problem of vulnerable road users (VRUs) attribute prediction which play such critical role for the intent prediction models of VRUs. We formulated the problem as a multi-task learning (MTL) image classification problem and we utilized a convolution neural network (ConvNet) based technique to exploit the commonality between two of the most important...
This study investigated the effect of a secondary task when a takeover request (TOR) is received from an automated driving car, with a focus on nature of the secondary task (active or passive). Participants were asked to engage in one type of secondary task during automated driving, and we measured 1) forward gaze and 2) latency after the TOR to switch from automated to manual operation. Playing a...
Highly automated driving tremendously reduces workload of drivers. However, the drivers may lose their visual attention to road ahead due to the out-of-the-loop problem. Haptic guidance has been developed to reduce drivers' workload while keeping the drivers in the control loop. The haptic guidance system continuously provides assistant torques on the steering wheel so that both the driver and the...
This paper describes the design of a new haptic shared steering control framework for automated driving systems. In this framework, the shared control problem is formulated as a constrained optimization problem which is solved online by a model predictive controller. Without driver's intervention, the system assumes automatic lane-keeping control. When the driver takes over control, by adapting the...
In order to realize really useful Level 2 driving automation systems that can be used in urban areas, it is a vital issue to develop a human-machine interface that assures drivers are able to recognize when to intervene into control. Visual information may not be appropriate for this purpose because the driver is required to monitor the traffic environment and the system status continuously. In this...
Conditionally automated driving systems may soon be available on the market. Even though these systems exempt drivers from the driving task for extended periods of time, drivers are expected to take back control when the automation issues a so-called take-over request. This study investigated the interaction between take-over request modality and type of non-driving task, regarding the driver's reaction...
In this paper, we present a method to estimate abstract parameters of high definition (HD) maps from sensor data. Parameters we estimate include the distance from ego-vehicle to road boundary, orientation of the ego-vehicle with respect to lanes, number of lanes, and street type. Our method is realized as a Convolutional Neural Network (CNN) that takes pre-processed sensor information in the form...
Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS) are being vigorously developed for improving traffic safety and transportation efficiency. AD and ADAS need to provide smooth and comfortable driving experiences to drivers and passengers as well as accommodation of other traffic. Individual drivers have different driving styles. This paper proposes a driver-adapted narrow road...
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