Dongpu Cao
Biographic Data
| ID | 4485353 |
|---|---|
| NAME | Dongpu Cao |
| GIVEN NAMES | Dongpu |
| FAMILY NAME | Cao |
| SIGNATURE | CAO D |
| AFFILIATIONS | Tsinghua University |
| ORCID | 0000-0003-2541-5272 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Tabular Learning-Based Traffic Event Prediction for Intelligent Social Transportation System
Accurate forecasting of future traffic is a critical contemporary problem for transportation research. However, it is difficult to understand the feature patterns of traffic events due to the complexity of the traffic environment, heterogeneous factors, and lack of abnormal samples. This article proposes a framework to integrate the social traffic data and use the TabNet model to facilitate the representation learning task in traffic event predic…
Efficient Driver Anomaly Detection via Conditional Temporal Proposal and Classification Network
Detecting driver inattentive behaviors is crucial for driving safety in a driver monitoring system (DMS). Recent works treat driver distraction detection as a multiclass action recognition problem or a binary anomaly detection problem. The former approach aims to classify a fixed set of action classes. Although specific distraction classes can be predicted, this approach is inflexible to detect unknown driver anomalies. The latter approach mixes …
CogEmoNet
Driver’s emotion recognition is vital to improving driving safety, comfort, and acceptance of intelligent vehicles. This article presents a cognitive-feature-augmented driver emotion detection method that is based on emotional cognitive process theory and deep networks. Different from the traditional methods, both the driver’s facial expression and cognitive process characteristics (age, gender, and driving age) were used as the inputs of the pro…
Modelling, Dynamics and Control of Electrified Vehicles
No prominent works on this page.
Modelling, Dynamics and Control of Electrified Vehicles
CogEmoNet
Driver’s emotion recognition is vital to improving driving safety, comfort, and acceptance of intelligent vehicles. This article presents a cognitive-feature-augmented driver emotion detection method that is based on emotional cognitive process theory and deep networks. Different from the traditional methods, both the driver’s facial expression and cognitive process characteristics (age, gender, and driving age) were used as the inputs of the pro…
Tabular Learning-Based Traffic Event Prediction for Intelligent Social Transportation System
Accurate forecasting of future traffic is a critical contemporary problem for transportation research. However, it is difficult to understand the feature patterns of traffic events due to the complexity of the traffic environment, heterogeneous factors, and lack of abnormal samples. This article proposes a framework to integrate the social traffic data and use the TabNet model to facilitate the representation learning task in traffic event predic…
Efficient Driver Anomaly Detection via Conditional Temporal Proposal and Classification Network
Detecting driver inattentive behaviors is crucial for driving safety in a driver monitoring system (DMS). Recent works treat driver distraction detection as a multiclass action recognition problem or a binary anomaly detection problem. The former approach aims to classify a fixed set of action classes. Although specific distraction classes can be predicted, this approach is inflexible to detect unknown driver anomalies. The latter approach mixes …
Artificial Intelligence (3 works) · Computer Science (3 works) · Engineering (2 works) · Machine learning (2 works) · Advanced Traffic Management System (1 works) · Aeronautics (1 works) · Anomaly detection (1 works) · Anomaly Detection Techniques and Applications (1 works) · Binary classification (1 works) · Cockpit (1 works)