Research on vehicle driving characteristics in narrow lanes based on different vehicle-following states

Han, Baorui; Zhu, Ruitong; Dong, Ren; Zhang, Mengfan; Song, Wanlu; Zhu, Zhenya

doi:10.1080/21680566.2022.2146776

Cited by 2 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where, p represents the true probability distribution, y represents the predicted probability distribution, and CE(p, y) represents the cross entropy. The parameter p t is defined as follows (10):…”

Section: Optimization Of Road Segmentation Modelmentioning

confidence: 99%

“…In the vehicle drivable area detection task, the result of object detection directly affects the ability of the vehicle to accurately recognize and understand the surrounding environment. On the other hand, road segmentation aims to assist intelligent vehicles in accurately identifying drivable areas [9,10]. By integrating object detection and road segmentation technologies, the vehicle's perception of road conditions can be enhanced.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on multitask model of object detection and road segmentation in unstructured road scenes

Gao,

Zhao,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

With the rapid development of artificial intelligence and computer vision technology, autonomous driving technology has become a hot area of concern. The driving scenarios of autonomous vehicles can be divided into structured scenarios and unstructured scenarios.Compared with structured scenes, unstructured road scenes lack the constraints of lane lines and traffic rules, and the safety awareness of traffic participants is weaker. Therefore, there are new and higher requirements for the environment perception tasks of autonomous vehicles in unstructured road scenes. The current research rarely integrates the target detection and road segmentation to achieve the simultaneous processing of target detection and road segmentation of autonomous vehicle in unstructured road scenes. Aiming at the above issues, a multitask model for object detection and road segmentation in unstructured road scenes is proposed. Through the sharing and fusion of the object detection model and road segmentation model, multitask model can complete the tasks of multi-object detection and road segmentation in unstructured road scenes while inputting a picture. Firstly, MobileNetV2 is used to replace the backbone network of YOLOv5, and multi-scale feature fusion is used to realize the information exchange layer between different features.Subsequently, a road segmentation model was designed based on the DeepLabV3+ algorithm.Its main feature is that it uses MobileNetV2 as the backbone network and combines the binary classification focus Loss function for network optimization. Then, we fused the object detection algorithm and road segmentation algorithm based on the shared MobileNetV2 network to obtain a multitask model and trained it on both the public dataset and the self-built dataset NJFU. The training results demonstrate that the multitask model significantly enhances the algorithm's execution speed by approximately 10 FPS while maintaining the accuracy of object detection and road segmentation. Finally, we conducted validation of the multitask model on an actual vehicle.

show abstract

Section: Optimization Of Road Segmentation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on multitask model of object detection and road segmentation in unstructured road scenes

Gao,

Zhao,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

How gaps are created during anticipation of lane changes

Chen

Knoop

Liu

et al. 2022

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

The pre-insertion process called anticipation is an essential component of a lane-changing manoeuvre. There is little empirical research regarding the impact of anticipation. Thus, this paper aims to explore the behaviour of the new follower (NF) in the target lane when it encounters anticipation by using new trajectory datasets. The changing magnitude of the reaction pattern is proposed to identify the NF's behaviour. We find that the anticipation significantly affects the NF's movement in terms of gap creation and speed reduction. Then, we conduct a detailed analysis of critical variables to reveal their relationship with the NF's behaviour. Following this, we develop binary logistic models to predict the NF's behaviour, resulting in a good performance. It also suggests that the NF's behaviour is highly related to the anticipation-related variables. The transferability test results show that this model can be directly used in different locations and times with satisfactory accuracy.

show abstract

Research on vehicle driving characteristics in narrow lanes based on different vehicle-following states

Cited by 2 publications

References 34 publications

Research on multitask model of object detection and road segmentation in unstructured road scenes

Research on multitask model of object detection and road segmentation in unstructured road scenes

How gaps are created during anticipation of lane changes

Contact Info

Product

Resources

About