CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for Safer Driver Assistance Systems

Greer, Ross; Lulua, Rakla,; Samveed, Desai,; Alofi, Afnan; Gopalkrishnan, Akshay; Trivedi, Mohan M.

doi:10.48550/arxiv.2301.05842

Cited by 1 publication

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“…Accurate detection and recognition of traffic lights is vital for an autonomous vehicle to observe and interact with its surroundings in a safe manner, communicating information relevant to predicting an agent's trajectory [1] [2] [3] [4] or enabling ADAS features [5]. In general, standard object detectors for traffic lights, signs, or pedestrians operated by proposing regions of interest, which involves considering a standard set of anchors or window centers within an image, and classifying the contents of the found region [6] [7].…”

Section: A Overviewmentioning

confidence: 99%

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

Greer

Isa

Deo

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

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One of the most important tasks for ensuring safe autonomous driving systems is accurately detecting road traffic lights and accurately determining how they impact the driver's actions. In various real-world driving situations, a scene may have numerous traffic lights with varying levels of relevance to the driver, and thus, distinguishing and detecting the lights that are relevant to the driver and influence the driver's actions is a critical safety task. This paper proposes a traffic light detection model which focuses on this task by first defining salient lights as the lights that affect the driver's future decisions. We then use this salience property to construct the LAVA Salient Lights Dataset, the first US traffic light dataset with an annotated salience property. Subsequently, we train a Deformable DETR object detection transformer model using Salience-Sensitive Focal Loss to emphasize stronger performance on salient traffic lights, showing that a model trained with this loss function has stronger recall than one trained without.

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