Lane Change Detection With Smartphones: A Steering Wheel-Based Approach

Panichpapiboon, Sooksan; Leakkaw, Puttipong

doi:10.1109/access.2020.2994352

Cited by 10 publications

(5 citation statements)

References 22 publications

(37 reference statements)

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“…A variety of machine learning techniques, including naive Bayes, decision trees, random forests, support vector machines, K-means, and long short-term memory (LSTM) is also commonly used for classifying the maneuvering events [6], [11], [13]. Finally, a probabilistic-based method such as a hidden Markov model (HMM) can also be used for maneuvering detection [14].…”

Section: Related Workmentioning

confidence: 99%

“…The simplest and most effective way to provide these ordinary vehicles with the sensing capability is to use the built-in sensors on smartphones. In fact, a number of smartphone-based maneuvering detection methods have been investigated [2]- [14]. Most of these detection methods typically rely on the measurements obtained from an accelerometer and a gyroscope.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Vehicle Maneuvering Detection With Digital Compass

Leakkaw

Panichpapiboon

2021

IEEE Access

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Vehicle maneuverings are important pieces of information for many applications such as traffic incident detection and driving behavior recognition. On a macroscopic level, an unusually large number of some maneuvering events (e.g., lane changes) may suggest that a road incident occurs. On a microscopic level, vehicle maneuverings can tell how each individual person drives (e.g., safely or unsafely). A number of smartphone-based maneuvering detection methods have been proposed. However, most of them typically rely on accelerometer and gyroscope, and thus require the smartphone to be placed at a specific position on a vehicle and in a specific orientation. In this work, we take a rather different approach from most of the existing methods. Particularly, we investigate how effective it is to detect vehicle maneuverings by relying only on a signal from a digital compass on a smartphone. To this end, we introduce a simple rule-based maneuvering detection method which only takes the heading angles measured by the digital compass as inputs. This allows the smartphone to be placed freely at any position on the vehicle and in any orientation. Our results show that the digital compass can be used to detect turns and u-turns extremely well, and its accuracy on lane change detection is at an acceptable level.INDEX TERMS Vehicle maneuvering detection, digital compass, magnetometer, mobile sensing.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Vehicle Maneuvering Detection With Digital Compass

Leakkaw

Panichpapiboon

2021

IEEE Access

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“…[3][4][5][6][7][8], such as fuzzy logic [3], support vector machine (SVM) [4], long short-term memory network and convolutional neural network (LSTM-CNN) [5], maneuver classification [6], and hidden Markov model [7,8]. Panichpapiboon and Leakkaw explore an approach to detect lane-change behavior using steering wheel angles extracted from the smart phone [9]. Zheng and Hansen propose an approach to detect lane-change behavior using the steering angle signal from CAN-bus [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles

Chen

et al. 2022

Computational Intelligence and Neuroscience

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The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

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“…There are many studies that use three-axis sensors. Some works are based on the sensor to develop applications such as tire pressure, tire load, and vehicle tracking [8][9][10]. The road surface is one of the conditions of the vehicle application.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Tire Mileage and Wear Using Measurement Data

et al. 2021

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Tire mileage and wear provide important information for vehicle applications. There are more and more studies discussing intelligent tires, but few focus on the role of tire mileage and wear. The conventional tire pressure monitoring system (TPMS) is one of the intelligent tire applications, but there has been no significant advancement in recent years in this regard. In order to increase the additional functions of intelligent tire applications, we propose a method that estimates the mileage and wear information of tires. The proposed method uses a three-axis sensor and a Hall sensor to implement the function. The proposed method also has a low power design to reduce the power consumption of the Hall sensor. The experimental results show the trend of tire wear status, rendering this method effective. This method also requires more accurate mileage information to support tire wear estimation. This experiment found that the correct rate of the proposed mileage estimation method is 99.4% and provides sufficient and correct mileage information for tire wear methods. If this method is used in autonomous vehicle applications, the autonomous control strategy algorithm has more conditions to plan the control strategy. The strategy system processes more meticulous control that increases the safety of autonomous vehicles.

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Lane Change Detection With Smartphones: A Steering Wheel-Based Approach

Cited by 10 publications

References 22 publications

Real-Time Vehicle Maneuvering Detection With Digital Compass

Real-Time Vehicle Maneuvering Detection With Digital Compass

A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles

Estimation of Tire Mileage and Wear Using Measurement Data

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