Enhancement of Vehicle Speed Estimation with Single Loop Detectors

An algorithm for real-time estimation of truck traffic in multilane freeways was proposed. The algorithm used data from single loop detectors—the most widely installed surveillance technology for urban freeways in the United States. The algorithm worked for those freeway locations that have a truck-free lane and exhibit high lane-to-lane speed correlation. These conditions are met by most urban freeway locations. The algorithm produced real-time estimates of the truck traffic volumes at the location. It also can be used to produce alternative estimates of the mean effective vehicle length, which can improve speed estimates from single loop detector data. The algorithm was tested with real freeway data and produced estimates of truck traffic volumes with only 5.7% error. It also captured the daily patterns of truck traffic and mean effective vehicle length. Applied to loop data on Interstate 710 near Long Beach, California, during the dockworkers’ lockout October 1 to 9, 2002, the algorithm found a 32% reduction in five-axle truck volume.

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

Electronics ◽

10.3390/electronics11020265 ◽

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.

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Cyber-Physical Attack Detection and Recovery based on RNN in Automotive Brake Systems

10.20944/preprints201811.0045.v1 ◽

2018 ◽

Author(s):

Jongho Shin ◽

Youngmi Baek ◽

Jaeseong Lee ◽

Seonghun Lee

Keyword(s):

Classification Accuracy ◽

Weighted Average ◽

Data Integrity ◽

Wheel Speed ◽

Attack Detection ◽

Cyber Attacks ◽

Speed Estimation ◽

Vehicle Speed ◽

Detection And Identification ◽

Fault Detection And Identification

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

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Real-Time Traffic Measurement from Single Loop Inductive Signatures

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1804-14 ◽

2002 ◽

Vol 1804 (1) ◽

pp. 98-106 ◽

Cited By ~ 33

Author(s):

Seri Oh ◽

Stephen G. Ritchie ◽

Cheol Oh

Keyword(s):

Real Time ◽

The United States ◽

Traffic Data ◽

Traffic Surveillance ◽

Vehicle Class ◽

Traffic Measurement ◽

Single Loop ◽

Loop Detectors ◽

Real Time Traffic ◽

Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

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