Toward a practical wall see-through system for drivers: How simple can it be?

Yasuda, Hiroshi; Ohama, Yoshihiro

doi:10.1109/ismar.2012.6402600

Cited by 10 publications

(3 citation statements)

References 2 publications

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“…Driver assistance around blind corners has been studied in the context of augmented/diminished reality [1,17,27]. Barnum et al .…”

Section: Related Workmentioning

confidence: 99%

BlindSpotNet: Seeing Where We Cannot See

Fukuda¹,

Hasegawa²,

Ishizaki³

et al. 2022

Preprint

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We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage point, we can proactively alert a manual driver or a self-driving system to potential causes of accidents (e.g., draw attention to a road region from which a child may spring out). Detecting blind spots in full 3D would be challenging, as 3D reasoning on the fly even if the car is equipped with LiDAR would be prohibitively expensive and error prone. We instead propose to learn to estimate blind spots in 2D, just from a monocular camera. We achieve this in two steps. We first introduce an automatic method for generating "ground-truth" blind spot training data for arbitrary driving videos by leveraging monocular depth estimation, semantic segmentation, and SLAM. The key idea is to reason in 3D but from 2D images by defining blind spots as those road regions that are currently invisible but become visible in the near future. We construct a large-scale dataset with this automatic offline blind spot estimation, which we refer to as Road Blind Spot (RBS) dataset. Next, we introduce BlindSpotNet (BSN), a simple network that fully leverages this dataset for fully automatic estimation of frame-wise blind spot probability maps for arbitrary driving videos. Extensive experimental results demonstrate the validity of our RBS Dataset and the effectiveness of our BSN.

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“…Driver assistance around blind corners has been studied in the context of augmented/diminished reality [1,17,27]. Barnum et al .…”

Section: Related Workmentioning

confidence: 99%

BlindSpotNet: Seeing Where We Cannot See

Fukuda¹,

Hasegawa²,

Ishizaki³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, Tachi et al investigated the idea of making parts of the driver's own vehicle translucent to reduce blind spots [9], similar to BMW [1]. Yasuda & Ohama instead looked at making the environment translucent with a special focus on walls for instance at intersection [11]. Lindemann & Rigoll investigated subjective perceptual aspects of making parts of the environment translucent [5].…”

Section: Related Workmentioning

confidence: 99%

Increasing driver awareness through translucency on windshield displays

Amersfoorth

Roefs

Bonekamp

et al. 2019

Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct

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“…In a simulation study, Charissis & Papanastasiou (2010) found that virtual representations of vehicles, lane edges, and driving directions resulted in less collisions under limited visibility conditions (e.g., fog) with sudden traffic congestion. Yasuda & Ohama (2012) enhanced drivers' attention by using x-ray vision metaphor at blind corners to reduce crossing collisions. Kim et al examined how AR HUDs can inform drivers with vehicles in blind spot (Kim, Wu, Gabbard, & Polys, 2013) and dynamics of cross traffic (Kim et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Virtual Road Signs

Rane

Kim

Marcano

et al. 2016

Proceedings of the Human Factors and Ergonomics Society Annual

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Studies have shown that experts are more sensitive to changes in the road scene than novice drivers, and use the driving patterns of other cars to infer important information. A tool that can help bridge the gap between experts and novices may be augmented reality (AR), which can be used to graphically overlay virtual information onto the real world that may not otherwise be easily inferred. In this paper, we propose an AR interface that aims to improve the sensation, attention, situation awareness and decision making of international drivers who are new to the United States (US). We present results of a preliminary study that identifies the needs of novice international drivers as well as an AR interface design created to support these needs. Contextual inquiry and analysis techniques were used to extract the needs of novice international drivers. Based on observations, iterative designs and a prototype were developed that merge AR and audio feedback. Lastly, the prototype was evaluated by two usability experts, who performed a heuristic walkthrough based on the principles of human information processing. The experts conclude that the interface has the potential of increasing the sensation, attention, situation awareness and decision making while reducing the mental workload of novice international drivers. Future work will include an empirical study to support the observations of the analytical evaluation presented herein.

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Toward a practical wall see-through system for drivers: How simple can it be?

Cited by 10 publications

References 2 publications

BlindSpotNet: Seeing Where We Cannot See

BlindSpotNet: Seeing Where We Cannot See

Increasing driver awareness through translucency on windshield displays

Virtual Road Signs

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