Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions. While measuring the overall progress of those solutions over time tends to be more and more established due to the new publicly available benchmark and standardized evaluation procedures, knowing how well existing predictors react to unseen data remains an unanswered question. This evaluation is imperative as serviceable crossing behavior predictors should be set to work in various scenarii without compromising pedestrian safety due to misprediction. To this end, we conduct a study based on direct cross-dataset evaluation. Our experiments show that current state-of-the-art pedestrian behavior predictors generalize poorly in cross-dataset evaluation scenarii, regardless of their robustness during a direct training-test set evaluation setting. In the light of what we observe, we argue that the future of pedestrian crossing prediction, e.g. reliable and generalizable implementations, should not be about tailoring models, trained with very little available data, and tested in a classical train-test scenario with the will to infer anything about their behavior in real life. It should be about evaluating models in a cross-dataset setting while considering their uncertainty estimates under domain shift.
Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
School of Computing Science, University of Glasgow（格拉斯哥大学计算机科学学院）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫研究所海因里希赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Korea University（高丽大学）；Max Planck Institut für Informatik（马克斯·普朗克计算机科学研究所）
MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting
Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), Shenzhen（广东省全主动系统控制理论与技术重点实验室，自动化与智能制造学院，南方科技大学（SUSTech），深圳）；Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, China（香港城市大学机械工程系，香港特别行政区，中国）
Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians
University of Warwick（华威大学）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂与沃里克郡大学医院国民保健服务信托）；Guy’s and St Thomas’ NHS Trust（盖伊和圣托马斯国民保健服务信托）；King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）
