Enhancement of road weather services using vehicle sensor data

Bogaerts, Toon; Watelet, Sylvain; Thoen, Chris; Coopman, Tom; Bergh, Joris Van den; Reyniers, Maarten; Seynaeve, Dirck; Casteels, Wim; Latré, Steven; Hellinckx, Peter

doi:10.1109/ccnc49033.2022.9700658

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…These measurements are validated by comparing the observations by our mobile sensors to meteorological forecasts. This is a continuation of our previous research [ 22 , 43 ]. Given the large amounts of generated data, artificial intelligence and machine learning are perfectly suited to improve the sensor accuracy [ 15 ].…”

Section: Materials and Methodssupporting

confidence: 84%

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Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model

Bogaerts

Watelet

Bruyne³

et al. 2022

Sensors

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Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.

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Section: Materials and Methodssupporting

confidence: 84%

“…Figure 5 shows an example of this comparison for the RWS of Stabroek and the following variables: RST, T2M, and RH. Verification for this RWS was first performed in [ 43 ] for a shorter verification period. Until 11 March 2021, the SARWS T2M and RH can originate from the Bpost cars or the INCA outputs.…”

Section: Resultsmentioning

confidence: 99%

Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model

Bogaerts

Watelet

Bruyne³

et al. 2022

Sensors

Self Cite

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“…Bogaerts et al [ 20 ] propose a model to enhance the current RWM by using vehicular sensor data and to enable real-time road weather warnings for local weather phenomena and dangerous road conditions. Vehicles are equipped with a CAN reader and external sensors and the data collected by the fleet are then sent to a cloud back-end using a data distribution framework.…”

Section: Related Workmentioning

confidence: 99%

“…Following that and using the same equipment as in [ 20 ], Bogaerts et al [ 21 ] demonstrated two examples of how artificial intelligence and machine learning can be leveraged to process data from a fleet of vehicles and obtain relevant weather information with an improved data quality compared to the raw measurements, using convolution neural networks to extract visibility and precipitation data from raw camera images. With this, a new RWM was presented to enrich the modeling of road weather with car data at a high spatiotemporal resolution.…”

Section: Related Workmentioning

confidence: 99%

A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity

Rocha

Teixeira

Vieira

et al. 2023

Sensors

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The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle’s On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs).

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“…Consequently, these factors give rise to the emergence of potential hazards and risks within the driving context. The external environment involves moving objects (motor vehicles, nonmotor vehicles, and pedestrians) [2,3], static environmental elements (road boundaries, lane separation lines) [4,5], dynamic traffic control signals (traffic lights) [6], and weather [7][8][9]. Wang et al constructed a unified driving safety field model that utilizes field theory to represent risks caused by drivers, vehicles, road conditions, and other traffic factors [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Visual-Based Approach for Driver’s Environment Perception and Quantification in Different Weather Conditions

Luo,

Liu,

Mei

et al. 2023

Applied Sciences

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The decision-making behavior of drivers during the driving process is influenced by various factors, including road conditions, traffic situations, weather conditions, and so on. However, our understanding and quantification of the driving environment are still very limited, which not only increases the risk of driving but also hinders the deployment of autonomous vehicles. To address this issue, this study attempts to transform drivers’ visual perception into machine vision perception. Specifically, the study provides a detailed decomposition of the elements constituting weather and proposes three environmental quantification indicators: visibility brightness, visibility clarity, and visibility obstruction rate. These indicators help us to describe and quantify the driving environment more accurately. Based on these indicators, a visual-based environmental quantification method is further proposed to better understand and interpret the driving environment. Additionally, based on drivers’ visual perception, this study extensively analyzes the impact of environmental factors on driver behavior. A cognitive assessment model is established to evaluate drivers’ cognitive abilities in different environments. The effectiveness and accuracy of the model are validated through driver simulation experiments, thereby establishing a communication bridge between the driving environment and driver behavior. This research achievement enables us to better understand the decision-making behavior of drivers in specific environments and provides some references for the development of intelligent driving technology.

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Enhancement of road weather services using vehicle sensor data

Cited by 4 publications

References 15 publications

Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model

Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model

A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity

A Visual-Based Approach for Driver’s Environment Perception and Quantification in Different Weather Conditions

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