Driver Attention Monitoring
3 papers with code • 0 benchmarks • 6 datasets
Driver attention monitoring is the task of monitoring the attention of a driver.
( Image credit: Predicting Driver Attention in Critical Situations )
Benchmarks
These leaderboards are used to track progress in Driver Attention Monitoring
DR(eye)VE
BDD-A
OC
HPD
GD
MAAD
Most implemented papers
Predicting Driver Attention in Critical Situations
pascalxia/driver_attention_prediction
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Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol---tracking eye movements during driving.
DADA: Driver Attention Prediction in Driving Accident Scenarios
1) With the semantic images, we introduce their semantic context features and verified the manifest promotion effect for helping the driver attention prediction, where the semantic context features are modeled by a graph convolution network (GCN) on semantic images; 2) We fuse the semantic context features of semantic images and the features of RGB frames in an attentive strategy, and the fused details are transferred over frames by a convolutional LSTM module to obtain the attention map of each video frame with the consideration of historical scene variation in driving situations; 3) The superiority of the proposed method is evaluated on our previously collected dataset (named as DADA-2000) and two other challenging datasets with state-of-the-art methods.
Gated Driver Attention Predictor
In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP).
