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Understanding what drivers look at is important for many applications,including driver training, monitoring, and assistance, as well as self-driving.Traditionally, factors affecting human visual attention have been divided intobottom-up (involuntary attraction to salient regions) and top-down (task- andcontext-driven). Although both play a role in drivers' gaze allocation, most ofthe existing modeling approaches apply techniques developed for bottom-upsaliency and do not consider task and context influences explicitly. Likewise,common driving attention benchmarks lack relevant task and context annotations.Therefore, to enable analysis and modeling of these factors for drivers' gazeprediction, we propose the following: 1) address some shortcomings of thepopular DR(eye)VE dataset and extend it with per-frame annotations for drivingtask and context; 2) benchmark a number of baseline and SOTA models forsaliency and driver gaze prediction and analyze them w.r.t. the newannotations; and finally, 3) a novel model that modulates drivers' gazeprediction with explicit action and context information, and as a resultsignificantly improves SOTA performance on DR(eye)VE overall (by 24\% KLD and89\% NSS) and on a subset of action and safety-critical intersection scenarios(by 10--30\% KLD). Extended annotations, code for model and evaluation will bemade publicly available.

Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocation