Zobrazeno 1 - 10
of 577
pro vyhledávání: '"Trivedi, Mohan"'
Autor:
Mohamed, Sondos, Zimmer, Walter, Greer, Ross, Ghita, Ahmed Alaaeldin, Castrillón-Santana, Modesto, Trivedi, Mohan, Knoll, Alois, Carta, Salvatore Mario, Marras, Mirko
Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address th
Externí odkaz:
http://arxiv.org/abs/2408.15637
Autor:
Greer, Ross, Trivedi, Mohan
This study investigates the use of trajectory and dynamic state information for efficient data curation in autonomous driving machine learning tasks. We propose methods for clustering trajectory-states and sampling strategies in an active learning fr
Externí odkaz:
http://arxiv.org/abs/2405.09049
Driver activity classification is crucial for ensuring road safety, with applications ranging from driver assistance systems to autonomous vehicle control transitions. In this paper, we present a novel approach leveraging generalizable representation
Externí odkaz:
http://arxiv.org/abs/2404.14906
Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning
Externí odkaz:
http://arxiv.org/abs/2404.12856
Vision-Language Models (VLMs) and Multi-Modal Language models (MMLMs) have become prominent in autonomous driving research, as these models can provide interpretable textual reasoning and responses for end-to-end autonomous driving safety tasks using
Externí odkaz:
http://arxiv.org/abs/2403.19838
Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities. However, these modalities operate at varying rates, posing challenges
Externí odkaz:
http://arxiv.org/abs/2403.00196
Autor:
Greer, Ross, Trivedi, Mohan
This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that autonomous vehicles struggle to navigate, necessitating
Externí odkaz:
http://arxiv.org/abs/2402.07320
Autor:
Ghita, Ahmed, Antoniussen, Bjørk, Zimmer, Walter, Greer, Ross, Creß, Christian, Møgelmose, Andreas, Trivedi, Mohan M., Knoll, Alois C.
The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remains. In this work, we fill the research gap using active learning for m
Externí odkaz:
http://arxiv.org/abs/2402.03235
Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness of entropy querying to select informative samp
Externí odkaz:
http://arxiv.org/abs/2401.16634
Autor:
Greer, Ross, Gopalkrishnan, Akshay, Mandadi, Sumega, Gunaratne, Pujitha, Trivedi, Mohan M., Marcotte, Thomas D.
About 30% of all traffic crash fatalities in the United States involve drunk drivers, making the prevention of drunk driving paramount to vehicle safety in the US and other locations which have a high prevalence of driving while under the influence o
Externí odkaz:
http://arxiv.org/abs/2309.08021