Dataset for Robust and Accurate Leading Vehicle Velocity Recognition
Recognition of the surrounding environment using a camera is an important technology in Advanced Driver-Assistance Systems and Autonomous Driving, and recognition technology is often solved by machine learning approaches such as deep learning in recent years. Machine learning requires datasets for learning and evaluation. To develop robust recognition technology in the real world, in addition to normal driving environment, data in environments that are difficult for cameras such as rainy weather or nighttime are essential. We have constructed a dataset that one can benchmark the technology, targeting the velocity recognition of the leading vehicle. This task is an important one for the Advanced Driver-Assistance Systems and Autonomous Driving. The dataset is available at https://signate.jp/competitions/657
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingBIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Estimation of Vehicular Velocity based on Non-Intrusive stereo camera
The paper presents a modular approach for the estimation of a leading vehicle's velocity based on a non-intrusive stereo camera where SiamMask is used for leading vehicle tracking, Kernel Density estimate (KDE) is used t…
Robust and Accurate Object Velocity Detection by Stereo Camera for Autonomous Driving
Although the number of camera-based sensors mounted on vehicles has recently increased dramatically, robust and accurate object velocity detection is difficult. Additionally, it is still common to use radar as a fusion s…
Autonomous DrivingObjectA Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems
Accurate lane change prediction can reduce potential accidents and contribute to higher road safety. Adaptive cruise control (ACC), lane departure avoidance (LDA), and lane keeping assistance (LKA) are some conventional …
PositionLiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements
Autonomous underwater vehicles (AUVs) are employed for marine applications and can operate in deep underwater environments beyond human reach. A standard solution for the autonomous navigation problem can be obtained by …
Autonomous NavigationAI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment
The most common type of accident on the road is a rear-end crash. These crashes have a significant negative impact on traffic flow and are frequently fatal. To gain a more practical understanding of these scenarios, it i…