Circular Image

Holger Caesar

34 records found

Radar-based perception has been gaining traction in recent years, supported by improvements in deep learning techniques. Low-level radar perception focuses on utilizing the denser radar signal data instead of the conventional point-cloud. Despite the recent focus on this data rep ...

GaussianFusion++

Adaptive Radar–Camera Fusion for Maritime 3D Detection

Autonomous Surface Vehicles (ASVs) must operate safely in dynamic and often cluttered maritime environments such as ports and inland waterways. Achieving reliable situational awareness in these settings remains challenging due to the limited availability of annotated datasets, th ...
Autonomous vessels offer potential benefits in safety, operational efficiency, and environmental impact, but require reliable perception systems to function independently. This thesis investigates the reliability of camera-based detection of maritime docks, a class of static but ...
The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. This paper presents an online 3D LiDAR semantic segmentation pipeline developed using our multi-sensor ‘SenseBike’ rese ...
Trustworthiness in Multilingual Large Language Mod- els (MLLMs) varies across languages, often explained by differences in pretraining resources. While this associa- tion with pretraining data is well established, we hypoth- esize that typological differences between languages al ...
When optimising the placement of sensors on an Autonomous Vehicle (AV), research often uses evolutionary algorithms, offering a flexible way to explore complex solution spaces with multiple candidate configurations. However, this approach limits the ability to optimise one partic ...
This paper presents a comprehensive and quantitative framework for enhancing the safety of autonomous vehicles by integrating sensor detection performance with braking dynamics under extreme weather conditions and variable road slopes. Using high-fidelity simulations in CARLA an ...
Building registry updates are essential for urban planning but remain a labor-intensive process. This thesis introduces PolyChange, an adaptation of the PolyBuilding model, to automate mutation delineation by integrating aerial imagery with reference maps to produce precise, vect ...
This paper presents a method that shows how a lidar-based 3D static environment can be constructed from a driving scenario and is used to aid in the creation of a digital twin from that scenario. Built with limited computational resources in mind, the resulting 3D static backgrou ...
Automotive radars are increasingly used in automated driving systems due to their cost effectiveness, ease of integration, and ability to withstand adverse weather conditions. Semantic segmentation for radar point clouds is a crucial step in radar pre-processing, which can be use ...
Currently, self-driving vehicles have trouble detecting partially and fully occluded objects such as pedestrians, vehicles, and static obstacles. It has been proven that a drone surveilling the area around the vehicle improves the vehicle's awareness of its surroundings. This wor ...
LiDAR technology is gaining popularity for use in 3D object detection, necessary for self-driving cars. However, due to class imbalances in state-of-the-art LiDAR datasets, detection algorithms often tend to lack performance in detecting cyclists. To address this issue, we introd ...
2D to 3D transfer learning has proven to be an effective method to transfer strong 2D representations into a 3D network. Recent advancements show that casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, such as through range projection, enables the proces ...
Training models for autonomous vehicles (AVs) necessitates substantial volumes of high-quality data due to the strong correlation between dataset size and model performance. However, acquiring such datasets is labor-intensive and expensive, requiring significant resources for col ...
Neural Radiance Fields (NeRFs) have showcased remarkable effectiveness in capturing complex 3D scenes and synthesizing novel viewpoints. By inherently capturing the entire scene in a compact representation, they offer a promising avenue for applications such as simulators, where ...
Supercritical CO2 (SCO2) is a promising alternative to traditional working fluids in heat pumps and power cycles due to its high density, thermal efficiency, and stability. These properties allow for the design of more compact and efficient equipment. Howeve ...

Product-ProtoNet

A simple architecture for classifying supermarket products, using just a few example images

Airlab, a collaboration between TU Delft and Ahold Delhaize, is developing Albert, a robot tailored to work in a complex supermarket environment. Key to Albert is a product detection and classification module that tells it what products to grasp and where they are located in a sh ...
Autonomous driving systems require robust and reliable perception across diverse environmental conditions, yet current approaches to 3D semantic occupancy prediction face challenges in adverse weather and lighting. In this paper, we present the first study on fusing 4D radar and ...
Autonomous navigation for Unmanned Surface Vessels (USVs) is essential for expanding their applications in maritime environments, where situational awareness and collision avoidance are critical. However, these environments present unique challenges for multi-object tracking (MOT ...
In autonomous driving, environmental perception, crucial for navigation and decision-making, depends on integrating data from multiple sensors like cameras and LiDAR. Camera-LiDAR fusion combines detailed imagery with precise depth, improving environmental awareness. Effective da ...