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With rapid development in hardware (sensors and processors) and AI algorithms, automated driving techniques have entered the public’s daily life and achieved great success in supporting human driving performance. However, due to the high contextual variations and temporal dynamics in pedestrian behaviors, the interaction between autonomous-driving cars and pedestrians remains challenging, impeding the development of fully autonomous driving systems. This paper focuses on predicting pedestrian intention with a novel transformer-based evidential prediction (TrEP) algorithm. We develop a transformer module towards the temporal correlations among the input features within pedestrian video sequences and a deep evidential learning model to capture the AI uncertainty under scene complexities. Experimental results on three popular pedestrian intent benchmarks have verified the effectiveness of our proposed model over the state-of-the-art. The algorithm performance can be further boosted by controlling the uncertainty level. We systematically compare human disagreements with AI uncertainty to further evaluate AI performance in confusing scenes. The code is released at https://github.com/zzmonlyyou/TrEP.git. 

Recent progress in data-driven vision and language-based tasks demands developing training datasets enriched with multiple modalities representing human intelligence. The link between text and image data is one of the crucial modalities for developing AI models. The development process of such datasets in the video domain requires much effort from researchers and annotators (experts and non-experts). Researchers re-design annotation tools to extract knowledge from annotators to answer new research questions. The whole process repeats for each new question which is timeconsuming. However, since the last decade, there has been little change in how the researchers and annotators interact with the annotation process. We revisit the annotation workflow and propose a concept of an adaptable and scalable annotation tool. The concept emphasizes its users’ interactivity to make annotation process design seamless and efficient. Researchers can conveniently add newer modalities to or augment the extant datasets using the tool. The annotators can efficiently link free-form text to image objects. For conducting human-subject experiments on any scale, the tool supports the data collection for attaining group ground truth. We have conducted a case study using a prototype tool between two groups with the participation of 74 non-expert people. We find that the interactive linking of free-form text to image objects feels intuitive and evokes a thought process resulting in a high-quality annotation. The new design shows ≈ 35% improvement in the data annotation quality. On UX evaluation, we receive above-average positive feedback from 25 people regarding convenience, UI assistance, usability, and satisfaction. 

This paper addresses the problem of detecting pedestrians using an enhanced object detection method. In particular, the paper considers the occluded pedestrian detection problem in autonomous driving scenarios where the balance of performance between accuracy and speed is crucial. Existing works focus on learning representations of unique persons independent of body parts semantics. To achieve a real-time performance along with robust detection, we introduce a body parts based pedestrian detection architecture where body parts are fused through a computationally effective constraint optimization technique. We demonstrate that our method significantly improves detection accuracy while adding negligible runtime overhead. We evaluate our method using a real-world dataset. Experimental results show that the proposed method outperforms existing pedestrian detection methods.