Papers Driver Attention Monitoring
“Driver Attention Monitoring” 태그가 달린 논문 14편 · 필터 해제
Where, What, Why: Towards Explainable Driver Attention Prediction
Modeling task-driven attention in driving is a fundamental challenge for both autonomous vehicles and cognitive science. Existing methods primarily predict where drivers look by generating spatial heatmaps, but fail to c…
Autonomous DrivingAutonomous VehiclesDriver Attention MonitoringLarge Language Model+2SecureGaze: Defending Gaze Estimation Against Backdoor Attacks
Gaze estimation models are widely used in applications such as driver attention monitoring and human-computer interaction. While many methods for gaze estimation exist, they rely heavily on data-hungry deep learning to a…
Driver Attention MonitoringGaze EstimationSTDA: Spatio-Temporal Dual-Encoder Network Incorporating Driver Attention to Predict Driver Behaviors Under Safety-Critical Scenarios
Accurate behavior prediction for vehicles is essential but challenging for autonomous driving. Most existing studies show satisfying performance under regular scenarios, but most neglected safety-critical scenarios. In t…
Autonomous DrivingDriver Attention MonitoringPredictionAHMF: Adaptive Hybrid-Memory-Fusion Model for Driver Attention Prediction
Accurate driver attention prediction can serve as a critical reference for intelligent vehicles in understanding traffic scenes and making informed driving decisions. Though existing studies on driver attention predictio…
Domain AdaptationDriver Attention MonitoringPredictionSaliency DetectionLatent Embedding Clustering for Occlusion Robust Head Pose Estimation
Head pose estimation has become a crucial area of research in computer vision given its usefulness in a wide range of applications, including robotics, surveillance, or driver attention monitoring. One of the most diffic…
ClusteringDriver Attention MonitoringHead Pose EstimationPose EstimationGated Driver Attention Predictor
Driver attention prediction implies the intention understanding of where the driver intends to go and what object the driver concerned about, which commonly provides a driving task-guided traffic scene understanding. Som…
Driver Attention MonitoringPredictionScene UnderstandingFBLNet: FeedBack Loop Network for Driver Attention Prediction
The problem of predicting driver attention from the driving perspective is gaining increasing research focus due to its remarkable significance for autonomous driving and assisted driving systems. The driving experience …
Autonomous DrivingDriver Attention MonitoringPredictionSaliency PredictionCoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)
The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preve…
Driver Attention MonitoringPredictionCoCAtt: A Cognitive-Conditioned Driver Attention Dataset
The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preve…
Driver Attention MonitoringPredictionDADA: Driver Attention Prediction in Driving Accident Scenarios
Driver attention prediction is becoming an essential research problem in human-like driving systems. This work makes an attempt to predict the driver attention in driving accident scenarios (DADA). However, challenges tr…
Driver Attention MonitoringPredictionScene UnderstandingDADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations. In saf…
Driver Attention MonitoringCombining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring
Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we investigate the combination of deep learning based methods and depth maps as input images to tack…
Deep LearningDriver Attention MonitoringFacial Landmark DetectionHead Pose Estimation+1Predicting Driver Attention in Critical Situations
Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so rare, collecting enough data for these si…
Autonomous DrivingDriver Attention MonitoringFrom Depth Data to Head Pose Estimation: a Siamese approach
The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the p…
Driver Attention MonitoringHead Pose EstimationPose EstimationPosition+1