A Multi-Semantic Driver Behavior Recognition Model of Autonomous Vehicles Using Confidence Fusion Mechanism
| Autor: | Yage Guo, Zhonghao Bai, Hongze Ren, Xiangyu Cheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK1001-1841
Control and Optimization business.industry Computer science Aggregate (data warehouse) Inference Cognition confidence fusion Machine learning computer.software_genre Object detection driver behavior recognition Production of electric energy or power. Powerplants. Central stations Action (philosophy) Control and Systems Engineering multi-semantic description TA401-492 Key (cryptography) False positive paradox Artificial intelligence business intelligent electric vehicles Materials of engineering and construction. Mechanics of materials computer Fusion mechanism |
| Zdroj: | Actuators Volume 10 Issue 9 Actuators, Vol 10, Iss 218, p 218 (2021) |
| ISSN: | 2076-0825 |
| DOI: | 10.3390/act10090218 |
| Popis: | With the rise of autonomous vehicles, drivers are gradually being liberated from the traditional roles behind steering wheels. Driver behavior cognition is significant for improving safety, comfort, and human–vehicle interaction. Existing research mostly analyzes driver behaviors relying on the movements of upper-body parts, which may lead to false positives and missed detections due to the subtle changes among similar behaviors. In this paper, an end-to-end model is proposed to tackle the problem of the accurate classification of similar driver actions in real-time, known as MSRNet. The proposed architecture is made up of two major branches: the action detection network and the object detection network, which can extract spatiotemporal and key-object features, respectively. Then, the confidence fusion mechanism is introduced to aggregate the predictions from both branches based on the semantic relationships between actions and key objects. Experiments implemented on the modified version of the public dataset Drive& Act demonstrate that the MSRNet can recognize 11 different behaviors with 64.18% accuracy and a 20 fps inference time on an 8-frame input clip. Compared to the state-of-the-art action recognition model, our approach obtains higher accuracy, especially for behaviors with similar movements. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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