17 citations · 31 across the 21 of their papers we have counts for
22 papers
doPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditioned Planning in Autonomous Driving
Parthib Roy, Yash Tandon, Marcus Blennemann +4
Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple stages of behavior, depend on fut…
When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems
Yash Tandon, Giovanni Tapia Lopez, Marcus Blennemann +2
Autonomous vehicles (AVs) are increasingly deployed in urban environments, yet their safety frameworks remain primarily designed around collision avoidance and minimal risk conditi…
Vision-Language Work Zone Intelligence for Safety-Critical Speed Regulation of Mixed-Autonomy Vehicles in Dynamic Environments
Angel Martinez-Sanchez, Kianna Ng, Wesley Maia +7
Temporary work-zone speed limits are communicated through visually inconsistent signage and are often missing from digital maps, creating safety risks for human drivers and automat…
Vision and Language: Novel Representations and Artificial intelligence for Driving Scene Safety Assessment and Autonomous Vehicle Planning
Ross Greer, Maitrayee Keskar, Angel Martinez-Sanchez +3
Vision-language models (VLMs) have recently emerged as powerful representation learning systems that align visual observations with natural language concepts, offering new opportun…
Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making
Ross Greer, Laura Fleig, Maitrayee Keskar +9
The looking-in-looking-out (LILO) framework has enabled intelligent vehicle applications that understand both the outside scene and the driver state to improve safety outcomes, wit…
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…
Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
Walter Zimmer, Ross Greer, Xingcheng Zhou +7
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as an unavoidable…
A New Perspective On AI Safety Through Control Theory Methodologies
Lars Ullrich, Walter Zimmer, Ross Greer +3
While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major co…
Towards Vision Zero: The TUM Traffic Accid3nD Dataset
Walter Zimmer, Ross Greer, Daniel Lehmberg +9
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as unavoidable an…
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…
PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems
Walter Zimmer, Ramandika Pranamulia, Xingcheng Zhou +2
In the context of Intelligent Transportation Systems (ITS), efficient data compression is crucial for managing large-scale point cloud data acquired by roadside LiDAR sensors. The…
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…
Structure Aware and Class Balanced 3D Object Detection on nuScenes Dataset
Sushruth Nagesh, Asfiya Baig, Savitha Srinivasan +2
3-D object detection is pivotal for autonomous driving. Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth informati…