no code implementations • 15 May 2024 • Ross Greer, Mohan Trivedi
This study investigates the use of trajectory and dynamic state information for efficient data curation in autonomous driving machine learning tasks.
no code implementations • 23 Apr 2024 • Ross Greer, Mathias Viborg Andersen, Andreas Møgelmose, Mohan Trivedi
In this paper, we present a novel approach leveraging generalizable representations from vision-language models for driver activity classification.
no code implementations • 19 Apr 2024 • Ross Greer, Bjørk Antoniussen, Andreas Møgelmose, Mohan Trivedi
In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection.
1 code implementation • 28 Mar 2024 • Akshay Gopalkrishnan, Ross Greer, Mohan Trivedi
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 traffic scene images and other data modalities.
no code implementations • 29 Feb 2024 • Mathias Viborg Andersen, Ross Greer, Andreas Møgelmose, Mohan Trivedi
The findings suggest the potential of generative models in addressing missing frames, advancing driver state monitoring for intelligent vehicles, and underscoring the need for continued research in model generalization and customization.
no code implementations • 11 Feb 2024 • Ross Greer, Mohan Trivedi
From the generated clusters, we further present methods for generating textual explanations of elements which differentiate scenes classified as novel from other scenes in the data pool, presenting qualitative examples from the clustered results.
no code implementations • 27 Jul 2023 • Akshay Gopalkrishnan, Ross Greer, Maitrayee Keskar, Mohan Trivedi
Vehicle light detection, association, and localization are required for important downstream safe autonomous driving tasks, such as predicting a vehicle's light state to determine if the vehicle is making a lane change or turning.
no code implementations • 26 Jul 2023 • Ross Greer, Akshay Gopalkrishnan, Maitrayee Keskar, Mohan Trivedi
Overall, this paper provides insights into the representation of vehicle lights and the importance of accurate annotations for training effective detection models in autonomous driving applications.
1 code implementation • 8 May 2023 • Ross Greer, Samveed Desai, Lulua Rakla, Akshay Gopalkrishnan, Afnan Alofi, Mohan Trivedi
It is critical for vehicles to prevent any collisions with pedestrians.
no code implementations • 8 May 2023 • Ross Greer, Akshay Gopalkrishnan, Jacob Landgren, Lulua Rakla, Anish Gopalan, Mohan Trivedi
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.
no code implementations • 30 Jan 2023 • Ross Greer, Mohan Trivedi
Multi-sensor frameworks provide opportunities for ensemble learning and sensor fusion to make use of redundancy and supplemental information, helpful in real-world safety applications such as continuous driver state monitoring which necessitate predictions even in cases where information may be intermittently missing.
no code implementations • 14 Jan 2023 • Ross Greer, Akshay Gopalkrishnan, Nachiket Deo, Akshay Rangesh, Mohan Trivedi
Next, we use a custom salience loss function, Salience-Sensitive Focal Loss, to train a Deformable DETR object detection model in order to emphasize stronger performance on salient signs.
no code implementations • 14 Jan 2023 • Ross Greer, Lulua Rakla, Anish Gopalan, Mohan Trivedi
Manual (hand-related) activity is a significant source of crash risk while driving.
no code implementations • 14 Jan 2023 • Ross Greer, Nachiket Deo, Akshay Rangesh, Pujitha Gunaratne, Mohan Trivedi
To make safe transitions from autonomous to manual control, a vehicle must have a representation of the awareness of driver state; two metrics which quantify this state are the Observable Readiness Index and Takeover Time.
no code implementations • 14 Jan 2023 • Ross Greer, Lulua Rakla, Samveed Desai, Afnan Alofi, Akshay Gopalkrishnan, Mohan Trivedi
Moreover, we use the number of correct advisories, false advisories, and missed advisories to define precision and recall performance metrics to evaluate CHAMP.
no code implementations • 25 May 2022 • Sushruth Nagesh, Asfiya Baig, Savitha Srinivasan, Akshay Rangesh, Mohan Trivedi
Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth information.
no code implementations • 25 May 2022 • Ross Greer, Mohan Trivedi
We demonstrate the algorithmic performance by analyzing three real-world datasets containing multiple periods of data collection for four-corner and two-corner intersections with marked and unmarked crosswalks.
no code implementations • 12 Nov 2020 • Ross Greer, Nachiket Deo, Mohan Trivedi
Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes.
no code implementations • 16 Sep 2019 • Daniela Ridel, Nachiket Deo, Denis Wolf, Mohan Trivedi
Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories.
2 code implementations • 1 May 2019 • Walter Zimmer, Akshay Rangesh, Mohan Trivedi
In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT).