1 code implementation • 5 Feb 2024 • Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer, Ross Greer, Christian Creß, Andreas Møgelmose, Mohan M. Trivedi, Alois C. Knoll
We propose ActiveAnno3D, an active learning framework to select data samples for labeling that are of maximum informativeness for training.
no code implementations • 30 Jan 2024 • Ross Greer, Bjørk Antoniussen, Mathias V. Andersen, Andreas Møgelmose, Mohan M. Trivedi
Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data.
no code implementations • 14 Sep 2023 • Ross Greer, Akshay Gopalkrishnan, Sumega Mandadi, Pujitha Gunaratne, Mohan M. Trivedi, Thomas D. Marcotte
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 of alcohol.
no code implementations • 2 Dec 2021 • Ross Greer, Jason Isa, Nachiket Deo, Akshay Rangesh, Mohan M. Trivedi
Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents.
no code implementations • 27 Jul 2021 • Akshay Rangesh, Nachiket Deo, Ross Greer, Pujitha Gunaratne, Mohan M. Trivedi
Using the augmented dataset, we develop and train take-over time (TOT) models that operate sequentially on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views, showing models trained on the augmented dataset to outperform the initial dataset.
no code implementations • 23 Apr 2021 • Akshay Rangesh, Nachiket Deo, Ross Greer, Pujitha Gunaratne, Mohan M. Trivedi
With increasing automation in passenger vehicles, the study of safe and smooth occupant-vehicle interaction and control transitions is key.
1 code implementation • 22 Mar 2021 • Hala Abualsaud, Sean Liu, David Lu, Kenny Situ, Akshay Rangesh, Mohan M. Trivedi
This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields.
Ranked #3 on Lane Detection on LLAMAS
no code implementations • 11 Jan 2021 • Akshay Rangesh, Pranav Maheshwari, Mez Gebre, Siddhesh Mhatre, Vahid Ramezani, Mohan M. Trivedi
This study follows many classical approaches to multi-object tracking (MOT) that model the problem using dynamic graphical data structures, and adapts this formulation to make it amenable to modern neural networks.
no code implementations • 6 May 2020 • Kaouther Messaoud, Nachiket Deo, Mohan M. Trivedi, Fawzi Nashashibi
The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure.
1 code implementation • 6 Feb 2020 • Akshay Rangesh, Bo-Wen Zhang, Mohan M. Trivedi
GPCycleGAN is based on the well-known CycleGAN approach - with the addition of a gaze classifier and a gaze consistency loss for additional supervision.
1 code implementation • 3 Jan 2020 • Nachiket Deo, Mohan M. Trivedi
We address the problem of forecasting pedestrian and vehicle trajectories in unknown environments, conditioned on their past motion and scene structure.
Ranked #10 on Trajectory Prediction on Stanford Drone
1 code implementation • 27 Jul 2019 • Akshay Rangesh, Mohan M. Trivedi
This paper provides a simple solution for reliably solving image classification tasks tied to spatial locations of salient objects in the scene.
no code implementations • 23 May 2019 • Nachiket Deo, Mohan M. Trivedi
We address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem.
no code implementations • 14 May 2019 • Daniela A. Ridel, Nachiket Deo, Denis Wolf, Mohan M. Trivedi
In this paper, we present a data-driven approach to implicitly model pedestrians' interactions with vehicles, to better predict pedestrian behavior.
1 code implementation • 16 Nov 2018 • Akshay Rangesh, Mohan M. Trivedi
Once identified, the "best fit" plane provides enough constraints to successfully construct the desired 3D detection box, without directly predicting the 6DoF pose of the object.
no code implementations • 14 Nov 2018 • Nachiket Deo, Mohan M. Trivedi
Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles.
no code implementations • 15 May 2018 • Nachiket Deo, Mohan M. Trivedi
To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles.
3 code implementations • 15 May 2018 • Nachiket Deo, Mohan M. Trivedi
Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic.
1 code implementation • 20 Apr 2018 • Akshay Rangesh, Mohan M. Trivedi
Tasks related to human hands have long been part of the computer vision community.
no code implementations • 3 Apr 2018 • Kevan Yuen, Mohan M. Trivedi
In the context of autonomous driving, where humans may need to take over in the event where the computer may issue a takeover request, a key step towards driving safety is the monitoring of the hands to ensure the driver is ready for such a request.
3 code implementations • 23 Feb 2018 • Akshay Rangesh, Mohan M. Trivedi
In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks.
no code implementations • 22 Feb 2018 • Siddharth, Akshay Rangesh, Eshed Ohn-Bar, Mohan M. Trivedi
This work addresses the task of accurately localizing driver hands and classifying the grasp state of each hand.
no code implementations • 8 Feb 2018 • Sourabh Vora, Akshay Rangesh, Mohan M. Trivedi
Finally, we evaluate our best performing model on the publicly available Columbia Gaze Dataset comprising of images from 56 subjects with varying head pose and gaze directions.
no code implementations • 5 Feb 2018 • Kevan Yuen, Mohan M. Trivedi
A key step to driver safety is to observe the driver's activities with the face being a key step in this process to extracting information such as head pose, blink rate, yawns, talking to passenger which can then help derive higher level information such as distraction, drowsiness, intent, and where they are looking.
no code implementations • 31 Jan 2018 • Sujitha Martin, Sourabh Vora, Kevan Yuen, Mohan M. Trivedi
The study and modeling of driver's gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode.
no code implementations • 24 Jan 2018 • Akshay Rangesh, Mohan M. Trivedi
The intelligent vehicle community has devoted considerable efforts to model driver behavior, and in particular to detect and overcome driver distraction in an effort to reduce accidents caused by driver negligence.
no code implementations • 19 Jan 2018 • Nachiket Deo, Akshay Rangesh, Mohan M. Trivedi
In this paper we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and inter-vehicle interaction.
no code implementations • 21 Sep 2017 • Akshay Rangesh, Kevan Yuen, Ravi Kumar Satzoda, Rakesh Nattoji Rajaram, Pujitha Gunaratne, Mohan M. Trivedi
Recent progress in autonomous and semi-autonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings.
no code implementations • 6 Jan 2017 • Eshed Ohn-Bar, Mohan M. Trivedi
We aim to study the modeling limitations of the commonly employed boosted decision trees classifier.
Ranked #33 on Face Detection on WIDER Face (Medium)
no code implementations • 12 Mar 2015 • Eshed Ohn-Bar, Mohan M. Trivedi
This paper studies efficient means for dealing with intra-category diversity in object detection.