scholarly journals Driving Speed Estimation and Trapped Drivers’ Detection inside Tunnels Using Distributed MIMO Bluetooth Devices

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 265
Author(s):  
Sotirios Kontogiannis ◽  
Anestis Kastellos ◽  
George Kokkonis ◽  
Theodosios Gkamas ◽  
Christos Pikridas
Keyword(s):  
Random Forests ◽  
Kalman Filtering ◽  
System Architecture ◽  
Evaluation Process ◽  
Speed Estimation ◽  
Resources Management ◽  
Loop Detectors ◽  
Driving Speed ◽  
Moving Vehicles ◽  
Classifier Training

Accidents in highway tunnels involving trucks carrying flammable cargoes can be dangerous, needing immediate confrontation to detect and safely evacuate the trapped people to lead them to the safety exits. Unfortunately, existing sensing technologies fail to detect and track trapped persons or moving vehicles inside tunnels in such an environment. This paper presents a distributed Bluetooth system architecture that uses detection equipment following a MIMO approach. The proposed equipment uses two long-range Bluetooth and one BLE transponder to locate vehicles and trapped people in motorway tunnels. Moreover, the detector’s parts and distributed architecture are analytically described, along with interfacing with the authors’ resources management system implementation. Furthermore, the authors also propose a speed detection process, based on classifier training, using RSSI input and speed calculations from the tunnel inductive loops as output, instead of the Friis equation with Kalman filtering steps. The proposed detector was experimentally placed at the Votonosi tunnel of the EGNATIA motorway in Greece, and its detection functionality was validated. Finally, the detector classification process accuracy is evaluated using feedback from the existing tunnel inductive loop detectors. According to the evaluation process, classifiers based on decision trees or random forests achieve the highest accuracy.

scholarly journals Kalman Filtering Applied to Induction Motor State Estimation

2020 ◽  
Author(s):  
Yassine Zahraoui ◽  
Mohamed Akherraz
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Speed Estimation ◽  
Stochastic Algorithm ◽  
State Variables ◽  
Extended Kalman Filtering ◽  
Filtering Theory ◽  
State Observation

This chapter presents a full definition and explanation of Kalman filtering theory, precisely the filter stochastic algorithm. After the definition, a concrete example of application is explained. The simulated example concerns an extended Kalman filter applied to machine state and speed estimation. A full observation of an induction motor state variables and mechanical speed will be presented and discussed in details. A comparison between extended Kalman filtering and adaptive Luenberger state observation will be highlighted and discussed in detail with many figures. In conclusion, the chapter is ended by listing the Kalman filtering main advantages and recent advances in the scientific literature.

scholarly journals Numerical simulation of pollutant dispersion in urban roadway tunnels

2017 ◽  
Vol 9 (1) ◽  
pp. 26-31 ◽  
Author(s):  
Jingliang Dong ◽  
Yao Tao ◽  
Yimin Xiao ◽  
Jiyuan Tu
Keyword(s):  
Air Quality ◽  
Traffic Congestion ◽  
Pollutant Dispersion ◽  
Piston Effect ◽  
Traffic Jam ◽  
Traffic Conditions ◽  
Toxic Emissions ◽  
Driving Speed ◽  
Moving Vehicles ◽  
Short Tunnel

Vehicular toxic emissions can easily contaminate the air quality of the enclosed tunnel environment, especially during rush hours with traffic jam events or low vehicle speeds, which poses serious health hazards to road utilizers. The piston effect generated by moving vehicles was normally considered adequate to discharge vitiated air out of short tunnel based on a typical driving speed. However, complex traffic conditions may yield unexpected consequences on in-tunnel air quality levels. This study numerically investigated the CO2 concentration to identify the in-tunnel pollutant dispersion under three traffic conditions including severe traffic congestion and traffic flow with low vehicle speeds. Fan conditions were considered to model the influence of mechanical winds on pollutant dispersion and comparison with vehicular piston effect was also performed. The results revealed elevated pollutant concentration regions were found at the vicinity of near-ground region and tunnel downstream. The vehicular piston effect can sufficiently remove the in-tunnel vehicular emissions when vehicles travel at relatively higher speed. However, pollutant accumulation occurs when vehicles are idling or moving at slow speed. Compared with traffic piston effect at high travelling speed, the mechanical ventilation of ceiling mounted fans only generate a limited contribution to the removal of emissions.

scholarly journals Real Time Speed Estimation of Moving Vehicles from Side View Images from an Uncalibrated Video Camera

Sensors ◽  
2010 ◽  
Vol 10 (5) ◽  
pp. 4805-4824 ◽  
Author(s):  
Sedat Doğan ◽  
Mahir Serhan Temiz ◽  
Sıtkı Külür
Keyword(s):  
Real Time ◽  
Video Camera ◽  
Speed Estimation ◽  
Side View ◽  
Moving Vehicles

scholarly journals Operational and Safety Evaluation of Freeways with Posted Minimum Speed Limit

2010 ◽  
Author(s):  
Victor Muchuruza ◽  
Renatus Mussa
Keyword(s):  
Safety Evaluation ◽  
Maximum Speed ◽  
Speed Limit ◽  
Crash Data ◽  
Minimum Speed ◽  
Driving Speed ◽  
Moving Vehicles ◽  
Slow Moving ◽  
The Mean ◽  
The Difference

An operational and safety evaluation was conducted in relation to the posting of the minimum speed limit of 40 mph and the maximum speed limit of 70 mph on the Florida rural interstate freeway system. The results showed that 57% of the recorded vehicles exceeded the maximum speed limit. Additionally, while only 0.14% of recorded vehicles had speeds below the 40 mph posted minimum speed limit, 9% of crash-involved vehicles were estimated to have speeds below 40 mph. The overrepresentation of slow-moving vehicles in the crash data suggests that even a small proportion of under-40-mph vehicles can have negative implications on safety. Thus, regulation of vehicle speeds at the lower end of the speed distribution is equally important. The second order polynomial model developed to estimate the risk of a vehicle being involved in a crash as a function of the speed deviation from the mean speed of traffic indicated that the minimum risk occurred when the driving speed was 8 mph above the mean speed, equal to the 85th percentile speed observed in the field. Further, the Poisson regression modeling indicated that the difference between the 85th and 15th percentile speeds had a positive effect on crashes.

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