Analyzing the relationship between distracted driving and eye movement using multimodal data collected during car driving

Human error is the leading cause of traffic accidents and originates from the distraction caused by various factors, such as the driver's physical condition and mental state. One of the significant factors causing driver distraction is the presence of stress. In a previous study, multiple stressors were used to examine distraction while driving. Multiple stressors were given to the driver and the corresponding driver biometric data were obtained, and a multimodal dataset was published thereafter. In this study, we reiterate the results of existing studies and investigated the relationship between gaze variability while driving and stressor intervention, which has not yet been examined. We also examined whether biometric and vehicle information can estimate the presence or absence of secondary tasks during driving.

Internet of vehicles security situation awareness based on intrusion detection protection systems

With the rise of technologies such as mobile Internet, 5G networks and artificial intelligence, the development of Internet of Vehicle Information Security (ICVS) has become the mainstream and direction for the future development of the automotive industry. ICVS, people, roads, clouds, and APP constitute a complex network of vehicles. As part of the Internet, vehicle networking will inevitably face various complex information security threats and risks. This paper aims to design a kind of security situation awareness of Internet of vehicles based on intrusion detection protection systems (IDPS). By collecting the security data of car, app and private cloud for big data analysis, the whole smart car security situation awareness system is constructed. The system can be used to analyze potential threats, send out warnings, and carry out emergency responses.

Analysis of Relationship between Electroencephalograms and Subjective Measurements for In-Vehicle Information System: A Preliminary Study

In this study, we explored the relationship between objective and subjective measures for usability evaluation in in-vehicle infotainment systems (IVISs). As a case study, four displays were evaluated based on cluster location and display orientation (that is, front–horizontal, front–vertical, right–horizontal, and right–vertical). Thirty-six participants performed tasks to manipulate the functions of the IVISs and data were collected through an electroencephalogram (EEG) sensor and questionnaire items. We analysed a model that estimated EEG-based objective indicators from subjective indicators. As a result, the objective indicators reflected the subjective indicators and were considered to explain the driver’s cognitive state. Although EEG data were collected from only four participants, this study proposed an experimental design that could be applied to the analysis of the relationship between the subject’s evaluation and EEG signals, as a preliminary study. We expect the experimental design and results of this study to be useful in analysing objective and subjective measures of usability evaluation.

Analyzing the delays of target lane vehicles caused by vehicle lane-changing operation

Vehicle lane-changing on urban roads is the most common traffic behavior, in which the driver changes the direction or increases the speed of the vehicle by changing its trajectory. However, in high-density traffic flow, when a vehicle changes lanes, a series of vehicles following the target vehicle in the target lane will be delayed. In this study, DJI Phantom 4 drones were used to vertically record the traffic on a road section. Tracker software was then used to extract vehicle information from the video taken by the drones, including the vehicle operating speeds, etc. SPSS 22 and Origin analysis software were then employed to analyze the correlations between different vehicle operating parameters. It was found that the operating speed of the first vehicle following the target vehicle in the target lane is related to the speeds and positions of both the target vehicle and the vehicle preceding it. Under the condition of high-density traffic flow, when the target vehicle is inserted into the target lane, the speed of the vehicles following the target vehicle in the target lane will change. To model this process, the corresponding Sine and DoseResp models were constructed. By calculating the delays of vehicles following the target vehicle in the target lane, it was concluded that the overall delay of the fleet is 3.9–9.5 s.

Capabilities and Limitations of Telematics for Vehicle Emissions Inventories

Eastern Research Group, Inc. evaluated the current state of personal vehicle telematics data with respect to emission inventory development, identifying relative strengths and weaknesses, and how these data could align better with the needs of emission modelers. A market survey of telematics firms provided an overview of available data, and identified several candidate sources for location-based and engine-based telematics data on personal vehicles. Data were then purchased from three different vendors: StreetLight Data, Moonshadow Mobile, and Otonomo. These data were applied in case studies conducted in the Denver metro area, U.S., to assess strengths and weaknesses of telematics for developing emission inventories. Case studies included using telematics to estimate regional vehicle miles traveled (VMT) for annual emission inventories; tracking the VMT impacts of COVID shutdown; generating location- and time-specific vehicle activity inputs for project scale “hot spot” air quality analysis; and estimating the distribution of fuel fill level from real-world data, which is important for evaporative emissions. These case studies confirmed that telematics can serve a growing range of emission inventory use cases, and use of these data may help improve emission inventory accuracy. However, there are also several limitations of the data to consider in preparing emission inventories; for example, it can be difficult to assess the representativeness of telematics data because of a lack of vehicle information. The authors encourage telematics firms to cater data products more directly to the needs of emission inventory modelers, to better harness the enormous potential of these data for refining vehicle emission inventory estimates.

Mitigating Broadcasting Storm Using Multihead Nomination Clustering in Vehicular Content Centric Networks

Vehicles are highly mobile nodes; therefore, they frequently change their topology. To maintain a stable connection with the server in high-speed vehicular networks, the handover process is restarted again to satisfy the content requests. To satisfy the requested content, a vehicular-content-centric network (VCCN) is proposed. The proposed scheme adopts in-network caching instead of destination-based routing to satisfy the requests. In this regard, various routing protocols have been proposed to increase the communication efficiency of VCCN. Despite disruptive communication links due to head vehicle mobility, the vehicles create a broadcasting storm that increases communication delay and packet drop fraction. To address the issues mentioned above in the VCCN, we proposed a multihead nomination clustering scheme. It extends the hello packet header to get the vehicle information from the cluster vehicles. The novel cluster information table (CIT) has been proposed to maintain several nominated head vehicles of a cluster on roadside units (RSUs). In disruptive communication links due to the head vehicle’s mobility, the RSU nominates the new head vehicle using CIT entries, resulting in the elimination of the broadcasting storm effect on disruptive communication links. Finally, the proposed scheme increases the successful communication rate, decreases the communication delay, and ensures a high cache success ratio on an increasing number of vehicles.