17 citations · 51 across the 27 of their papers we have counts for
27 papers
MTR-VP: Towards End-to-End Trajectory Planning through Context-Driven Image Encoding and Multiple Trajectory Prediction
Maitrayee Keskar, Mohan Trivedi, Ross Greer
We present a method for trajectory planning for autonomous driving, learning image-based context embeddings that align with motion prediction frameworks and planning-based intentio…
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors
Walter Zimmer, Ross Greer, Xingcheng Zhou +7
Road traffic injuries are the leading cause of death for people aged 5-29, resulting in about 1.19 million deaths each year. To reduce these fatalities, it is essential to address…
Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection
Sondos Mohamed, Walter Zimmer, Ross Greer +6
Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditi…
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving
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. We propose methods for cluster…
Driver Activity Classification Using Generalizable Representations from Vision-Language Models
Ross Greer, Mathias Viborg Andersen, Andreas Møgelmose +1
Driver activity classification is crucial for ensuring road safety, with applications ranging from driver assistance systems to autonomous vehicle control transitions. In this pape…
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
Ross Greer, Bjørk Antoniussen, Andreas Møgelmose +1
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 scen…
Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving
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 reason…
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras
Mathias Viborg Andersen, Ross Greer, Andreas Møgelmose +1
Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities.…
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets
Ross Greer, Mohan Trivedi
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…
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection
Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer +5
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 remai…
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
Ross Greer, Bjørk Antoniussen, Mathias V. Andersen +2
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 demons…
Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol
Ross Greer, Akshay Gopalkrishnan, Sumega Mandadi +3
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 locatio…
Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations
Akshay Gopalkrishnan, Ross Greer, Maitrayee Keskar +1
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…
Patterns of Vehicle Lights: Addressing Complexities in Curation and Annotation of Camera-Based Vehicle Light Datasets and Metrics
Ross Greer, Akshay Gopalkrishnan, Maitrayee Keskar +1
This paper explores the representation of vehicle lights in computer vision and its implications for various tasks in the field of autonomous driving. Different specifications for…
Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations
Ross Greer, Akshay Gopalkrishnan, Jacob Landgren +3
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 act…
Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis
Ross Greer, Samveed Desai, Lulua Rakla +3
It is critical for vehicles to prevent any collisions with pedestrians. Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Au…
CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for Safer Driver Assistance Systems
Ross Greer, Lulua Rakla, Samveed Desai +3
Vehicles are constantly approaching and sharing the road with pedestrians, and as a result it is critical for vehicles to prevent any collisions with pedestrians. Current methods f…
(Safe) SMART Hands: Hand Activity Analysis and Distraction Alerts Using a Multi-Camera Framework
Ross Greer, Lulua Rakla, Anish Gopalan +1
Manual (hand-related) activity is a significant source of crash risk while driving. Accordingly, analysis of hand position and hand activity occupation is a useful component to und…
Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction
Ross Greer, Nachiket Deo, Akshay Rangesh +2
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 Obs…
Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context
Ross Greer, Akshay Gopalkrishnan, Nachiket Deo +2
Detecting road traffic signs and accurately determining how they can affect the driver's future actions is a critical task for safe autonomous driving systems. However, various tra…
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition
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 applicati…
From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets
Ross Greer, Mohan Trivedi
In this work, we contribute an EM algorithm for estimation of corner points and linear crossing segments for both marked and unmarked pedestrian crosswalks using the detections of…
On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset
Ross Greer, Jason Isa, Nachiket Deo +2
Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents. While all static scene elements are a sou…
Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation
Akshay Rangesh, Nachiket Deo, Ross Greer +2
Understanding occupant-vehicle interactions by modeling control transitions is important to ensure safe approaches to passenger vehicle automation. Models which contain contextual,…