13 citations · 31 across the 8 of their papers we have counts for
8 papers
Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings
Alexander Fertig, Karthikeyan Chandra Sekaran, Lakshman Balasubramanian +1
As autonomous vehicles are rolled out, measures must be taken to ensure their safe operation. In order to supervise a system that is already in operation, monitoring frameworks are…
Person Re-ID in 2025: Supervised, Self-Supervised, and Language-Aligned. What Works?
Lakshman Balasubramanian
Person Re-Identification (ReID) remains a challenging problem in computer vision. This work reviews various training paradigm and evaluates the robustness of state-of-the-art ReID…
Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction
Alexander Fertig, Lakshman Balasubramanian, Michael Botsch
Trajectory prediction is crucial to advance autonomous driving, improving safety, and efficiency. Although end-to-end models based on deep learning have great potential, they often…
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
Jonas Wurst, Lakshman Balasubramanian, Michael Botsch +1
Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity meas…
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios
Lakshman Balasubramanian, Jonas Wurst, Robin Egolf +3
Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the rep…
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity
Lakshman Balasubramanian, Jonas Wurst, Michael Botsch +1
Traffic scenario categorisation is an essential component of automated driving, for e.\,g., in motion planning algorithms and their validation. Finding new relevant scenarios witho…
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch +1
An understanding and classification of driving scenarios are important for testing and development of autonomous driving functionalities. Machine learning models are useful for sce…
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder
Jonas Wurst, Lakshman Balasubramanian, Michael Botsch +1
Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A tr…