Alerting the drivers about road signs with poor visual saliency

Simon, Ludovic; Tarel, Jean-Philippe; Brémond, Roland

doi:10.1109/ivs.2009.5164251

Cited by 28 publications

(22 citation statements)

References 21 publications

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“…The extracted information however is not directly usable for many applications as there are concerns that simply passing present signs to the driver merely leads to an increased cognitive load [15]. Some researchers try to tackle this problem directly using image based approaches like saliency estimation [11]. We argue that additional knowledge about the driver's attention, his goals and the traffic scene context (other relevant objects and relations to them) is needed to solve the issue satisfactorily.…”

Section: Related Workmentioning

confidence: 96%

Relevance estimation of traffic elements using Markov logic networks

Nienhüser

Gumpp

Zöllner

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Complex traffic situations e.g. at intersections consist of many traffic participants, traffic elements and relations between them. The behavior of participants is constrained by implicit and explicit traffic rules. We are interested in estimating whether a given traffic element -a traffic sign, a traffic light -is relevant in the current driving situation, i.e. affects the set of possible legal actions. A wide variety of properties influences the relevance. The route to take for example affects which traffic lights are relevant and the current weather situation affects whether a speed limit restricted by a supplementary sign is in effect. We use first-order logic to model such relations and apply reasoning to decide upon the relevance of static traffic elements. The need for perfect information is alleviated with the help of Markov logic networks, reconciling hard decision rules on the one hand and uncertainty intrinsic to the environment perception process on the other hand. The evaluation of twelve intersection scenes shows very promising results for the relevance estimation of traffic lights: Markov logic networks are able to judge whether enough information is available and determine the relevant traffic lights reliably in such cases.

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Section: Related Workmentioning

confidence: 96%

Relevance estimation of traffic elements using Markov logic networks

Nienhüser

Gumpp

Zöllner

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…Reference [10] is another large driver attention dataset, but only six coarse gaze regions were annotated and the exterior scene was not recorded. References [24] and [27] contain accurate driver attention maps made by averaging eye movements collected from human observers in-lab with simulated driving tasks. But the stimuli were static driving scene images and the sizes of their datasets are small (40 frames and 120 frames, respectively).…”

Section: Driver Attention Datasetsmentioning

confidence: 99%

Predicting Driver Attention in Critical Situations

Xia

Zhang

Kim

et al. 2019

Lecture Notes in Computer Science

140

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Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol-tracking eye movements during driving. Here, we first propose a new in-lab driver attention collection protocol and introduce a new driver attention dataset, Berkeley DeepDrive Attention (BDD-A) dataset, which is built upon braking event videos selected from a large-scale, crowd-sourced driving video dataset. We further propose Human Weighted Sampling (HWS) method, which uses human gaze behavior to identify crucial frames of a driving dataset and weights them heavily during model training. With our dataset and HWS, we built a driver attention prediction model that outperforms the state-of-the-art and demonstrates sophisticated behaviors, like attending to crossing pedestrians but not giving false alarms to pedestrians safely walking on the sidewalk. Its prediction results are nearly indistinguishable from ground-truth to humans. Although only being trained with our in-lab attention data, the model also predicts in-car driver attention data of routine driving with state-of-the-art accuracy. This result not only demonstrates the performance of our model but also proves the validity and usefulness of our dataset and data collection protocol.

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“…b) Appearance and size of a traffic sign: The appearance of a traffic sign affects its visibility, so considering its intensity and color would be effective for visibility estimation [2], [3]. In addition, a licensed driver has learned and memorized the representative appearances of traffic signs as templates, although there are several kinds of colors and shapes of traffic signs.…”

Section: ) Local Featuresmentioning

confidence: 99%

“…({kdoman,y-mekada}@sist.chukyo-u.ac.jp) 2 Graduate School of Information Science, Nagoya University, Japan ({ide,murase}@is.nagoya-u.ac.jp) 3 Information and Communications Headquarters, Nagoya University, Japan (ddeguchi@nagoya-u.jp) 4 Faculty of Economics and Information, Gifu Shotoku Gakuen University, Japan (ttakahashi@gifu.shotoku.ac.jp) 5 DENSO CORPORATION, Japan a traffic scene. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of traffic sign visibility considering local and global features in a driving environment

Doman

Deguchi

Takahashi

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-This paper proposes a camera-based visibility estimation method for a traffic sign. The visibility here indicates how a visual target is easy to be detected and recognized by a human driver (not a machine). This research aims at realizing a nuisance-free driver assistance system which sorts out information depending on the visibility of a visual target, in order to prevent driver distraction. Our previous study on estimating the visibility of a traffic sign considered only the effect of the local region around a target, assuming the situation that a driver's gaze is around it. The proposed method integrates both the local features and global features in a driving environment without such an assumption. The global features evaluate the positional relationships between traffic signs and the appearance around the fixation point of a driver's gaze, which considers the effect of the driver's entire field of view. Experimental results showed the effectiveness of incorporating the global features for estimating the visibility of a traffic sign.

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Alerting the drivers about road signs with poor visual saliency

Cited by 28 publications

References 21 publications

Relevance estimation of traffic elements using Markov logic networks

Relevance estimation of traffic elements using Markov logic networks

Predicting Driver Attention in Critical Situations

Estimation of traffic sign visibility considering local and global features in a driving environment

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