Lane-Changing Recognition of Urban Expressway Exit Using Natural Driving Data

Zhao, Lei; Xu, Tao; Zhang, Zhishun; Hao, Yue

doi:10.3390/app12199762

Cited by 10 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the research on methods for identifying traffic congestion, most scholars analyze from a micro perspective. Zhao et al [24] devised a model based on an enhanced clustering algorithm to predict lane congestion. Yang [25] created an internal grid congestion model, and Kong et al [26] developed a model based on floating car data to identify congested roads.…”

Section: Literature Review 21 Congestion Recognitionmentioning

confidence: 99%

Analysis of Urban Congestion Traceability: The Role of the Built Environment

Li,

Wang,

Chen

et al. 2024

Land

View full text Add to dashboard Cite

Analyzing the factors influencing traffic congestion is essential for urban planning and coordinated development. Previous research frequently focuses on the internal aspects of traffic systems, often overlooking the impact of external factors on congestion sources. Therefore, this study utilizes a geospatial dataset and mobile signaling data, firstly applying the Fuzzy C-Means (FCM) algorithm to identify congested roads of different levels and trace the localization of travelers’ origins on regional congested roads. Furthermore, it employs the LightGBM method to study the influence of the built environment of various congestion sources on network-level congestion. The findings are as follows: (1) There is a positive correlation between traffic congestion and geographical location, with congestion predominantly caused by a few specific plots and demonstrating a concentrated trend in city centers. (2) Residential population density is the most critical factor, accounting for over 12% of the congestion contribution, followed by road density and working population density. (3) Both residential and working population densities show a non-linear positive correlation with congestion contribution, while the mixture of land use displays a non-linear V-shaped influence. Additionally, when residential population density is between 8000 and 11,000, it notably exacerbates congestion contribution. Significantly, by emphasizing land use considerations in traffic system analysis, these findings illuminate the intricate linkages between urban planning and traffic congestion, advocating for a more comprehensive approach to urban development strategies.

show abstract

Section: Literature Review 21 Congestion Recognitionmentioning

confidence: 99%

Analysis of Urban Congestion Traceability: The Role of the Built Environment

Li,

Wang,

Chen

et al. 2024

Land

View full text Add to dashboard Cite

show abstract

“…The POSS-V included GPS/IMU, a steering angle sensor and a panoramic camera. Other researchers made use of UAVs to collect natural driving-track data [22]. A fixed-base driving simulator was also utilized by researchers [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The logic from other researchers is introduced to extract the lane changing data. A lane change begins and ends when the longitudinal moving distance of the vehicle is less than 0.1 m within 10 frames [22]. Based on this logic, the study extracts the lane changing data from the dataset.…”

Section: Data Extraction and Processingmentioning

confidence: 99%

Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

Zhang

Lou

et al. 2023

Electronics

View full text Add to dashboard Cite

The increasing number of vehicles has caused traffic conditions to become increasingly complicated in terms of safety. Emerging autonomous vehicles (AVs) have the potential to significantly reduce crashes. The advanced driver assistance system (ADAS) has received widespread attention. Lane keeping and lane changing are two basic driving maneuvers on highways. It is very important for ADAS technology to identify them effectively. The lane changing maneuver recognition has been used to study traffic safety for many years. Different models have been proposed. With the development of technology, machine learning has been introduced in this field with effective results. However, models which require a lot of physical data as input and unaffordable sensors lead to the high cost of AV platforms. This impedes the development of AVs. This study proposes a model of lane changing maneuver recognition based on a distinct set of physical data. The driving scenario from the natural vehicle trajectory dataset (i.e., HighD) is used for machine learning. Acceleration and velocity are extracted and labeled as physical data. The normalized features are then input into the k-nearest neighbor (KNN) classification model. The trained model was applied to another set of data and received good results. The results show that based on the acceleration features, the classification accuracy of lane keeping (LK), lane changing to the left (LCL) and lane changing to the right (LCR) is 100%, 97.89% and 96.19%.

show abstract

“…Zhao et al collected natural driving trajectory data in freeway diverging areas using unmanned aerial vehicles and applied K-means++ to cluster the driving styles in lane-changing sections. They achieved an accuracy of 93% using a random forest model for driving style identification and prediction [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP

Cheng,

Gao,

Jia

2023

Applied Sciences

View full text Add to dashboard Cite

To enhance the lane-changing safety of autonomous vehicles, it is crucial to accurately identify the driving styles of human drivers in scenarios involving the coexistence of autonomous and human-driven vehicles, aiming to avoid encountering vehicles exhibiting hazardous driving patterns. In this study, based on the real traffic flow data from the Next Generation Simulation (NGSIM) dataset in the United States, 301 lane-changing vehicles that meet the criteria are selected. Six evaluation parameters are chosen, and principal component analysis (PCA) is employed for dimensionality reduction in the data. The K-means algorithm is then utilized to cluster the driving styles, classifying them into three categories. Finally, ant colony optimization (ACO) of a backpropagation (BP) neural network model was constructed, utilizing the dimensionality reduction results as inputs and the clustering results as outputs for the purpose of driving style recognition. Simulation experiments are conducted using MATLAB Version 9.10 (R2021a) for comparative analysis. The results indicate that the constructed ACO-BP model achieved an overall recognition accuracy of 96.7%, significantly higher than the recognition accuracies of the BP, artificial neural network (ANN), and gradient boosting machine (GBM) models. The ACO-BP model also exhibited the fastest recognition speed among the four models. Moreover, the ACO-BP model shows varied improvements in recognition accuracy for each of the three driving styles, with an increase of 13.7%, 4.4%, and 4.3%, respectively, compared to the BP model. The simulation results validate the high accuracy, real-time capability, and classification effectiveness of this model in driving style recognition, providing new insights for this field.

show abstract

Lane-Changing Recognition of Urban Expressway Exit Using Natural Driving Data

Cited by 10 publications

References 22 publications

Analysis of Urban Congestion Traceability: The Role of the Built Environment

Analysis of Urban Congestion Traceability: The Role of the Built Environment

Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP

Contact Info

Product

Resources

About