Introspection of dnn-based perception functions in automated driving systems: State-of-the-art and open research challenges
Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
Quantitative identification of driver distraction: A weakly supervised contrastive learning approach
Accurate recognition of driver distraction is significant for the design of human-machine
cooperation driving systems. Existing studies mainly focus on classifying varied distracted …
cooperation driving systems. Existing studies mainly focus on classifying varied distracted …
Vision-language models can identify distracted driver behavior from naturalistic videos
Recognizing the activities causing distraction in real-world driving scenarios is critical for
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
A comparative analysis of decision-level fusion for multimodal driver behaviour understanding
Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-
vehicle interaction but such systems face substantial obstacles as they need to capture …
vehicle interaction but such systems face substantial obstacles as they need to capture …
On trustworthy decision-making process of human drivers from the view of perceptual uncertainty reduction
Humans are experts at making decisions for challenging driving tasks with uncertainties.
Many efforts have been made to model the decision-making process of human drivers at the …
Many efforts have been made to model the decision-making process of human drivers at the …
Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments
Deep learning-based models are at the top of most driver observation benchmarks due to
their remarkable accuracies but come with a high computational cost, while the resources …
their remarkable accuracies but come with a high computational cost, while the resources …
On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars
W Morales-Alvarez, N Certad… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
For driver observation frameworks, clean datasets collected in controlled simulated
environments often serve as the initial training ground. Yet, when deployed under real …
environments often serve as the initial training ground. Yet, when deployed under real …
Boldness-Recalibration for Binary Event Predictions
AP Guthrie, CT Franck - The American Statistician, 2024 - Taylor & Francis
Probability predictions are essential to inform decision making across many fields. Ideally,
probability predictions are (i) well calibrated,(ii) accurate, and (iii) bold, that is, spread out …
probability predictions are (i) well calibrated,(ii) accurate, and (iii) bold, that is, spread out …