In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing emotional state one of most challenging and important tasks. Most previous studies focused on facial expression recognition state. However, while driving, many factors are preventing drivers from revealing emotions their faces. To address this problem, we propose a deep learning-based real emotion recognizer (DRER), which algorithm recognize drivers’ that cannot be completely identified based...

Despite the advancement of advanced driver assistance systems (ADAS) and autonomous driving systems, surpassing threshold level 3 automation remains a challenging task. Level requires assuming full responsibility for vehicle's actions, necessitating acquisition safer more interpretable cues. To approach 3, we propose novel method detecting vehicles their brake light status, which is crucial visual cue relied upon by human drivers. Our proposal consists two main components. First, introduce...

Among human affective behavior research, facial expression recognition research is improving in performance along with the development of deep learning. For improved performance, not only past images but also future should be used corresponding images, there are obstacles to application this technique real-time environments. In paper, we propose causal affect prediction network (CAPNet), which uses predict valence and arousal. We train CAPNet learn inference between arousal through...

As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current datasets are limited by inconsistencies in collected data and inferred emotional state annotations others. To overcome this limitation, we propose a collection system that collects multimodal during real-world driving. The proposed includes self-reportable HMI application into which directly inputs their state. Data was completed without any accidents for over 122 h of...

It is necessary to calibrate the hydraulic pressure of shift control develop an automatic transmission (AT), and this calibration process entails a subjective quality assessment by experienced engineers. An objective methodology has been explored for long time replace engineer. The most recent data-based model attained nearly human-like performance. However, preparing large number data labels required supervised learning limitations. This study proposes unsupervised anomaly detection address...

With the development of big data and deep learning technologies, research on predicting human affects in wild using neural networks is being actively conducted. Many researchers use image audio together to improve affect prediction performance. However, synchronization between has not yet been achieved. Moreover, many different ways can be employed annotate affects, annotations datasets are identical. The cannot utilized supervised without annotation task predicted. This study proposes a...

Driver's hands on/off detection is very important in current autonomous vehicles for safety. Several studies have been conducted to create a precise algorithm. Although many proposed various approaches, they some limitations, such as robustness and reliability. Therefore, we propose deep learning model that utilizes in-vehicle data. We also established data collection system, which collects are auto-labeled efficient reliable acquisition. For robust devised confidence logic prevents...

Due to the collection of big data and development deep learning, research predict human emotions in wild is being actively conducted. We designed a multi-task model using ABAW dataset valence-arousal, expression, action unit through audio face images at real world. trained from incomplete label by applying knowledge distillation technique. The teacher was as supervised learning method, student output soft label. As result we achieved 2.40 Multi Task Learning task validation dataset.

Among human affective behavior research, facial expression recognition research is improving in performance along with the development of deep learning. However, for improved performance, not only past images but also future should be used corresponding images, there are obstacles to application this technique real-time environments. In paper, we propose causal affect prediction network (CAPNet), which uses predict valence and arousal. We train CAPNet learn inference between arousal through...

본 논문에서는 딥러닝 기반의 차량 경로예측 모델의 예측 정확도와 예측시간 사이의 Trade-off 관계에서 성능을 개선하기 위한 전파과정 간소화 방안을 제안하였다. 문제에서 기술을 활용하면서 점점 높은 정확도의 예측이 가능해지고 있지만, 반면에 복잡도가 증가하면서 예측시간이 길어지는 문제를 발생시킨다. 이 해결하기 위해 기존의 기반 state-of-the-art 모델에 여러 가지 적용하고 실험을 통해 예측시간을 측정하였다. PC 및 임베디드 환경에서의 검증을 통하여 주변차량의 Dynamic Motion 특성을 추출하는 과정을 간소화할 경우, 정확도 손실 없이 환경에서는 15.7%, 2.1% 단축했다.