Libo Cao

Cong Duan, Zixuan Liu, Jiahao Xia et al.

Deep neural networks enable real-time monitoring of in-vehicle drivers, facilitating the timely prediction of distractions, fatigue, and potential hazards. This technology is now integral to intelligent transportation systems. Recent research has exposed unreliable cross-dataset driver behavior recognition due to a limited number of data samples and background noise. In this paper, we propose a Score-Softmax classifier, which reduces the model overconfidence by enhancing category independence. Imitating the human scoring process, we designed a two-dimensional dynamic supervisory matrix consisting of one-dimensional Gaussian-smoothed labels. The dynamic loss descent direction and Gaussian smoothing increase the uncertainty of training to prevent the model from falling into noise traps. Furthermore, we introduce a simple and convenient multi-channel information fusion method;it addresses the fusion issue among arbitrary Score-Softmax classification heads. We conducted cross-dataset experiments using the SFDDD, AUCDD, and the 100-Driver datasets, demonstrating that Score-Softmax improves cross-dataset performance without modifying the model architecture. The experiments indicate that the Score-Softmax classifier reduces the interference of background noise, enhancing the robustness of the model. It increases the cross-dataset accuracy by 21.34%, 11.89%, and 18.77% on the three datasets, respectively. The code is publicly available at https://github.com/congduan-HNU/SSoftmax.

Xiexing Feng, Libo Cao, Yunxian Zhang et al.

Driver distraction has become a major threat to the road safety, and the globally booming taxi-hailing application introduces new source of distraction to drivers. Although various in-vehicle information systems (IVIS) have been studied extensively, no documentation exists objectively measuring the extent to which interacting with taxi-hailing application during driving impacts drivers' behavior. To fill this gap, a simulator-based study was conducted to synthetically compare the effects that different output modalities (visual, audio, combined visual-audio) and input modalities (baseline, manual, speech) imposed on the driving performance. The results show that the visual output introduced more negative effects on driving performance compared to audio output. In the combined output, visual component dominated the effects imposed on the longitudinal control and hazard detection; audio component only exacerbated the negative effects of visual component on the lateral control. Speech input modality was overall less detrimental to driving performance than manual input modality, especially reflected in the drivers' quicker reaction to hazard events. The visual-manual interaction modality most severely impaired the hazard detecting ability, while also led to strong compensative behaviors. The audio-speech and visual-speech modality associated with more smooth lateral control and faster response to hazard events respectively compared to other modality. These results could be applied to improve the design of not only the taxi-hailing application, but also other input-output balanced IVIS.