Evaluation of Video Masked Autoencoders' Performance and Uncertainty Estimations for Driver Action and Intention Recognition

Traffic fatalities remain among the leading death causes worldwide. To reduce this figure, car safety is listed as one of the most important factors. To actively support human drivers, it is essential for advanced driving assistance systems to be able to recognize the driver's actions and intentions. Prior studies have demonstrated various approaches to recognize driving actions and intentions based on in-cabin and external video footage. Given the performance of self-supervised video pre-trained (SSVP) Video Masked Autoencoders (VMAEs) on multiple action recognition datasets, we evaluate the performance of SSVP VMAEs on the Honda Research Institute Driving Dataset for driver action recognition (DAR) and on the Brain4Cars dataset for driver intention recognition (DIR). Besides the performance, the application of an artificial intelligence system in a safety-critical environment must be capable to express when it is uncertain about the produced results. Therefore, we also analyze uncertainty estimations produced by a Bayes-by-Backprop last-layer (BBB-LL) and Monte-Carlo (MC) dropout variants of an VMAE. Our experiments show that an VMAE achieves a higher overall performance for both offline DAR and end-to-end DIR compared to the state-of-the-art. The analysis of the BBB-LL and MC dropout models show higher uncertainty estimates for incorrectly classified test instances compared to correctly predicted test instances

Surrogate Deep Learning to Estimate Uncertainties for Driver Intention Recognition

Real-world applications of artificial intelligence that can potentially harm human beings should be able to express uncertainty about the made predictions. Probabilistic deep learning (DL) methods (e.g., variational inference [VI], VI last layer [VI-LL], Monte-Carlo [MC] dropout, stochastic weight averaging - Gaussian [SWA-G], and deep ensembles) can produce a predictive uncertainty but require expensive MC sampling techniques. Therefore, we evaluated if the probabilistic DL methods are uncertain when making incorrect predictions for an open-source driver intention recognition dataset and if a surrogate DL model can reproduce the uncertainty estimates. We found that all probabilistic DL methods are significantly more uncertain when making incorrect predictions at test time, but there are still instances where the models are very certain but completely incorrect. The surrogate DL models trained on the MC dropout and VI uncertainty estimates were capable of reproducing a significantly higher uncertainty estimate when making incorrect predictions.CC BY-NC-SA 4.0CORRESPONDING AUTHOR: K. VELLENGA (e-mail: [email protected])This work was supported by the Intention Recognition in Real Time for Automotive 3D Situation Awareness (IRRA) Project (https://www.vinnova.se/p/intention-recognition-i-realtid-for-automotive-3d-situation-awareness-irra/).Intention recognition for real-time automotive 3D situation awarenes

Driver intention recognition : state-of-the-art review

Every year worldwide more than one million people die and a further 50 million people are injured in traffic accidents. Therefore, the development of car safety features that actively support the driver in preventing accidents, is of utmost importance to reduce the number of injuries and fatalities. However, to establish this support it is necessary that the advanced driver assistance system (ADAS) understands the driver’s intended behavior in advance. The growing variety of sensors available for vehicles together with improved computer vision techniques, hence led to increased research directed towards inferring the driver’s intentions. This article reviews 64 driver intention recognition studies with regard to the maneuvers considered, the driving features included, the AI methods utilized, the achieved performance within the presented experiments, and the open challenges identified by the respected researchers. The article provides a high level analysis of the current technology readiness level of driver intention recognition technology to address the challenges to enable reliable driver intention recognition, such as the system architecture, implementation, and the purpose of the technology.CC BY-NC-ND 4.0CORRESPONDING AUTHOR: K. VELLENGA (e-mail: [email protected])This work was supported by the Intention Recognition in Real Time for Automotive 3D Situation Awareness (IRRA) Project (https://www.vinnova.se/p/intention-recognition-i-realtid-for-automotive-3d-situation-awareness-irra/).Intention recognition for real-time automotive 3D situation awarenes

Autophagy Proteins ATG5 and ATG7 Are Essential for the Maintenance of Human CD34+ Hematopoietic Stem-Progenitor Cells

Autophagy is a highly regulated catabolic process that involves sequestration and lysosomal degradation of cytosolic components such as damaged organelles and misfolded proteins. While autophagy can be considered to be a general cellular housekeeping process, it has become clear that it may also play cell type-dependent functional roles. In this study, we analyzed the functional importance of autophagy in human hematopoietic stem/progenitor cells (HSPCs), and how this is regulated during differentiation. Western blot-based analysis of LC3-II and p62 levels, as well as flow cytometry-based autophagic vesicle quantification, demonstrated that umbilical cord blood-derived CD34+/CD38- immature hematopoietic progenitors show a higher autophagic flux than CD34+/CD38+ progenitors and more differentiated myeloid and erythroid cells. This high autophagic flux was critical for maintaining stem and progenitor function since knockdown of autophagy genes ATG5 or ATG7 resulted in reduced HSPC frequencies in vitro as well as in vivo. The reduction in HSPCs was not due to impaired differentiation, but at least in part due to reduced cell cycle progression and increased apoptosis. This is accompanied by increased expression of p53, proapoptotic genes BAX and PUMA, and the cell cycle inhibitor p21, as well as increased levels of cleaved caspase-3 and reactive oxygen species. Taken together, our data demonstrate that autophagy is an important regulatory mechanism for human HSCs and their progeny, reducing cellular stress and promoting survival