Lane-Change Detection From Steering Signal Using Spectral Segmentation and Learning-Based Classification

Zheng, Yang; Hansen, John H. L.

doi:10.1109/tiv.2017.2708600

Cited by 79 publications

(28 citation statements)

References 30 publications

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“…Comparisons of different model variants on chosen data permit us to make a conclusion on the adaptation to varying parameters and show a quality improvement through the physical model. Yang and John [7] propose a machine learning-based segmentation and classification algorithm, which consists of three phases. The first phase is to preprocess and to prefilter so that it can diminish noise and get rid of clear left and right turning events.…”

Section: Related Work 21 Vehicle Machine Learningmentioning

confidence: 99%

An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm

Son¹,

Jeong²,

Lee³

2019

KSII TIIS

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A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

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Section: Related Work 21 Vehicle Machine Learningmentioning

confidence: 99%

An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm

Son¹,

Jeong²,

Lee³

2019

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…There has also been a recent interest in smart vehicle technologies such as advanced driver assistance systems (ADASs) and autonomous driving, and studies have been conducted on various related technologies [ 1 , 2 ]. These studies are concerned with recognizing or predicting human driving patterns, drowsy driving, driver behavior, and driving intentions [ 3 , 4 , 5 , 6 , 7 , 8 ]. There are two representative approaches to studies on driver technologies, namely use of a vehicle simulator and experimenting with a real vehicle [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis

Kim

Baek

2020

Sensors

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Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.

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“…However, these tracking constraints can be overcome by applying filters to constrain the detected motion of heavy-duty trucks. [14][15][16] Therefore, it is possible to smooth the random noise introduced by motion-detection sensors and improve the accuracy of truck tracking and subsequent motion behavior recognition. In addition, trucks commonly travel along the same routes and make routine stops, such as for cargo loading and unloading, which leads to low-sampling-rate trajectories where the average time interval between consecutive sample points is greater than 10 s. As a result, the raw trajectories of trucks following highly structured routes in urban areas have fewer sample points than those of other vehicles such as taxies.…”

Section: Introductionmentioning

confidence: 99%

Tracking and climbing behavior recognition of heavy-duty trucks on roadways

Tang

Jia

Duan

et al. 2020

International Journal of Distributed Sensor Networks

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The tracking and behavior recognition of heavy-duty trucks on roadways are keys for the development of automated heavy-duty trucks and an advanced driver assistance system. The spatiotemporal information of trucks from trajectory tracking and motions learnt from behavior analysis can be employed to predict possible driving risks and generate safe motion to avoid roadway accidents. This article presents a unified tracking and behavior recognition algorithm that can model the mobility of heavy-duty trucks on long inclined roadways. Random noise within the sampled elevation data is addressed by time-based segmentation to extract time-continuous samples at geographical locations. A Kalman filter is first used to distinguish error offsets from random noise and to estimate the distribution of truck elevations for different time intervals. A Markov chain Monte Carlo model is then applied to classify truck behaviors based on the change in elevation between two geographical locations. A heavy-duty truck mobility (HVMove) model is constructed based on the map information to apply the roadway geometry to the tracking and behavior recognition algorithm. We develop an extended Metropolis-Hastings algorithm to tune the parameters of the HVMove model. The proposed model is verified and evaluated through extensive experiments based on a real-world trajectory dataset covering sections of an expressway and national and provincial highways. From the experimental results, we conclude that the HVMove model provides sufficient accuracy and efficiency for automated heavy-duty trucks and advanced driver assistance system applications. In addition, HVMove can generate maps with the elevation information marked automatically.

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Lane-Change Detection From Steering Signal Using Spectral Segmentation and Learning-Based Classification

Cited by 79 publications

References 30 publications

An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm

An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm

Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis

Tracking and climbing behavior recognition of heavy-duty trucks on roadways

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