Model-based winter road classification

Casselgren, Johan; Sjödahl, Mikael; LeBlanc, James

doi:10.1504/ijvsmt.2012.048941

Cited by 17 publications

(13 citation statements)

References 9 publications

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“…Our work includes Road eye, a laser-based sensor capable of detecting different types of surface contamination, and a surface temperature sensor mounted on a vehicle instead of being mounted on the roadside as in [31]. In other papers, the properties of this sensor is tested and evaluated [32,33]. In this paper, we present its use in a larger context, namely as a possible vehicle-based sensor for the RCIS.…”

Section: Related Workmentioning

confidence: 99%

Reusable road condition information system for traffic safety and targeted maintenance

Casselgren¹,

Bodin

2017

IET Intelligent Transport Systems

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Section: Related Workmentioning

confidence: 99%

Reusable road condition information system for traffic safety and targeted maintenance

Casselgren¹,

Bodin

2017

IET Intelligent Transport Systems

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“…The operating principle of a three-wavelength road-condition sensor (TRCS) is the active transmission of an infrared light beam on the road surface and detection of the backscattered signal at three selected wavelengths. Johan Casselgren detected changes in the depths of water and ice road cover and classified different types of ice by utilizing polarized shortwave infrared light and a sensor consisting of three laser diodes with wavelengths of 980 nm, 1310 nm, and 1550 nm [9]. Patrik Jonsson classified dry, wet, snowy, and icy road conditions using a sensor consisting of three IR detectors with peak sensitivities at 960 nm, 1550 nm, and 1950 nm [10].…”

Section: Introductionmentioning

confidence: 99%

“…These changes cause the measurement state to deviate from the calibration state, resulting in a measured voltage that is anomalous compared to the calibration voltage. Existing studies that have been conducted to address this problem have focused on how to improve the robustness of qualitative classification by the sensor to road-covering types [9,11]. One solution is to assume that the intensity changes due to bumps and shocks are the same for all detection wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Calibration of Three-Wavelength Road-Condition Sensor Using a Dual-Wavelength Response Ratio

Yang

2022

IEEE Access

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Assessing the condition of a road exposed to the atmosphere is crucial for safety. A threewavelength road-condition sensor is a noncontact sensor that can be used for qualitative recognition and quantitative measurement of road conditions. However, the sensor is not robust to anomalous changes in the light intensity received that can be caused by several factors (such as changes in distance to the measuring point from a road bump or pothole). These changes cause the measurement state to deviate from the calibration state, reducing the sensor detection accuracy. However, existing studies on reducing the influence of the abovementioned problems on road-condition detection have only focused on how to ensure robustness in the qualitative classification of road-covering types, and no studies have been performed on how to ensure robustness in the quantitative measurement of the road-covering thickness. Thus, a quantitative calibration method for three-wavelength road-condition sensors using a dual-wavelength response ratio is proposed in this study. This method consists of using the response voltage ratio of two wavelengths to replace the response voltage of one wavelength during the quantitative calibration of sensors, making quantitative measurement insensitive to the deviation between measurement and calibration states. Comparison experiments were performed using the three-wavelength road-condition sensor calibrated by different quantitative methods, and an analysis was performed on the influence of the deviation of the measurement state from the calibration state on the quantitative measurement results and the measured covering thickness under changes in the measurement state. The experimental results verified the effectiveness of the novel approach compared with the traditional quantitative calibration method.

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“…Example applications include: health care and elderly care [3], security (policemen, firemen etc. ), industrial application, as shown by Karnouskos et al [4], vehicle testing [5] and winter road conditioning [6]. Typical requirements for these types of applications ranges from a few data transmissions per hour (posture, position, temperature etc) to continuues data transmission with sample rates of several kHz (vibration, audio, CAN-bus, etc).…”

Section: Introductionmentioning

confidence: 99%

Enabling Cloud Connectivity for Mobile Internet of Things Applications

Pereira

Eliasson

Kyusakov

et al. 2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

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Abstract-The number of small embedded devices connected to the Internet is increasing. This growth is mostly due to the large number of Internet of Things (IoT) deployments, with applications such as: industrial monitoring, home automation, and others. One common aspect with the majority of application areas is the lack of mobility. Most IoT devices are stationary and often use IEEE 802.15.4/6LoWPAN solutions. When a high level of mobility is required, the use of IEEE 802.15.4 is not possible without adding additional hardware for the user to carry.In this article, a holistic network architecture consisting of heterogeneous devices is presented. The architecture is composed of Embedded Internet Systems (EIS) and uses standard communication protocols. One important feature is the use of the Service-oriented architecture (SOA) paradigm. The use of SOA, by utilization of the CoAP protocol and standard services, enables the proposed architecture to exchange sensorand actuator data with an Internet-based cloud as well as a user's local cloud consisting of sensor IoT devices, smart phones and laptops. Another component of the architecture is a webbased human-machine interface for configuration, monitoring and visualization of sensor and actuator data using emerging web technologies for structured data processing.Results from experiments and real-world tests show that the proposed architecture can support sample rates of up to several kHz while enabling sensor data to be transmitted to SOA services in real time. This proves that the use of SOA, and RESTful web services in particular, is feasible on resourceconstrained platforms while supporting true mobility.

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Model-based winter road classification

Cited by 17 publications

References 9 publications

Reusable road condition information system for traffic safety and targeted maintenance

Reusable road condition information system for traffic safety and targeted maintenance

Quantitative Calibration of Three-Wavelength Road-Condition Sensor Using a Dual-Wavelength Response Ratio

Enabling Cloud Connectivity for Mobile Internet of Things Applications

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