Hazards Prioritization With Cognitive Attention Maps for Supporting Driving Decision-Making

Autonomous vehicles and driver assistance systems achieve satisfactory perception of surroundings but lack cognitive capabilities to conduct knowledge-based risk assessment and key focus cognition. Therefore, we propose the initial research that simulates human cognition to decipher traffic scenarios, to direct attention allocation for driving safety. Firstly, our system imitates cognitive processes including sensing, perceiving, reasoning, and judging, as the pipeline to deliver cognitive understanding beyond sensory perception. Secondly, diverse information and knowledge embedded in our criteria, fuzzy systems, and neural networks vitalizes the cognitive processes and thus fosters interpretability and trustworthiness. It goes beyond the visual features and differs from the utilization of condition-action knowledge in trigger rules design. Thirdly, our system achieves a holistic understanding of the entire scene beyond foreground road users or obstacles. It also stresses cognitive-level 'prominent hazards' that impact driving safety and deserve priority attentions and provident strategies, beyond visual saliencies or collision-related risks. Experimental comparisons with state-of-the-art models on 22 metrics on two datasets revealed that our system exhibited the overall smallest deviation from ground truths, justifying the effectiveness of our proposed cognition imitation in understanding driving scenarios. Our better results than baseline annotations further verified the capability in assisting situation awareness. The robustness in diverse environmental conditions, alertness to multitype hazards, and the conformity to knowledge indicate the interpretability and trustworthiness in offering forewarnings and directing attentions.