Driver and Pedestrian Mutual Awareness for Path Prediction in Intelligent Vehicles

This thesis addresses the sensor-based perception of driver and pedestrian to improve joint path prediction of ego-vehicle and pedestrian based on mutual awareness in the domain of intelligent vehicles.<br/><br/>According to the World Health Organization (WHO), more than half of global traffic deaths are among Vulnerable Road Users (VRUs), such...

IntrApose: Monocular Driver 6 DOF Head Pose Estimation Leveraging Camera Intrinsics

We present intrApose, a novel method for continuous 6 DOF head pose estimation from a single camera image without prior detection or landmark localization. We argue that using camera intrinsics alongside the intensity information is essential for accurate pose estimation. The proposed head pose estimation framework is crop-aware and scale...

Microwave-Based Quantum Control and Coherence Protection of Tin-Vacancy Spin Qubits in a Strain-Tuned Diamond-Membrane Heterostructure

Robust spin-photon interfaces in solids are essential components in quantum networking and sensing technologies. Ideally, these interfaces combine a long-lived spin memory, coherent optical transitions, fast and high-fidelity spin manipulation, and straightforward device integration and scaling. The tin-vacancy center (SnV) in diamond is a...

Protein chain collapse modulation and folding stimulation by GroEL-ES

The collapse of polypeptides is thought important to protein folding, aggregation, intrinsic disorder, and phase separation. However, whether polypeptide collapse is modulated in cells to control protein states is unclear. Here, using integrated protein manipulation and imaging, we show that the chaperonin GroEL-ES can accelerate the folding...

Driver and Pedestrian Mutual Awareness for Path Prediction and Collision Risk Estimation

We present a novel method for vehicle-pedestrian path prediction that takes into account the awareness of the driver and the pedestrian towards each other. The method jointly models the paths of vehicle and pedestrian within a single Dynamic Bayesian Network (DBN). In this DBN, sub-graphs model the environment and entity-specific context cues...

Hash-based hierarchical caching and layered filtering for interactive previews in global illumination rendering

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our...

DD-Pose: A large-scale Driver Head Pose Benchmark

We introduce DD-Pose, the Daimler TU Delft Driver Head Pose Benchmark, a large-scale and diverse benchmark for image-based head pose estimation and driver analysis. It contains 330k measurements from multiple cameras acquired by an in-car setup during naturalistic drives. Large out-of-plane head rotations and occlusions are induced by complex...

Deep end-to-end 3D person detection from Camera and Lidar

We present a method for 3D person detection from camera images and lidar point clouds in automotive scenes. The method comprises a deep neural network which estimates the 3D location and extent of persons present in the scene. 3D anchor proposals are refined in two stages: a region proposal network and a subsequent detection network.For both...

Coastal protection by a small scale river plume against oil spills in the Northern Gulf of Mexico

The Deepwater Horizon oil spill damaged some beaches along the Northern Gulf of Mexico (NGoMex) coast more than others, possibly related to the presence of natural protection mechanisms. In order to optimize future mitigation efforts to protect the coast, these mechanisms should be understood. The NGoMex coast is characterized by relatively...

Monotone trends in the distribution of climate extremes

The generalized Pareto distribution (GPD) is often used in the statistical analysis of climate extremes. For a sample of independent and identically distributed observations, the parameters of the GPD can be estimated by the maximum likelihood (ML) method. In this paper, we drop the assumption of identically distributed random variables. We...

Reconstitution of basic mitotic spindles in spherical emulsion droplets

Mitotic spindle assembly, positioning and orientation depend on the combined forces generated by microtubule dynamics, microtubule motor proteins and cross-linkers. Growing microtubules can generate pushing forces, while depolymerizing microtubules can convert the energy from microtubule shrinkage into pulling forces, when attached, for...

Trends in Moderate Rainfall Extremes: A Regional Monotone Regression Approach

Rainfall extremes are thought to have increased over recent years. Typically linear trends have been considered to describe the temporal evolution of high quantiles of the daily rainfall distribution. For long records it is important to allow more flexibility. Quantile regression methods are available to estimate monotone trends for single...

Projections of precipitation extremes based on a regional, non-stationary peaks-over-threshold approach: A case study for the Netherlands and north-western Germany

Projections of extreme precipitation are of great importance, considering the potential severe impacts on society. In this study a recently developed regional, non-stationary peaks-over-threshold approach is applied to two transient simulations of the RACMO2 regional climate model for the period 1950–2100, driven by two different general...

A regional peaks-over-threshold model in a nonstationary climate

Regional frequency analysis is often used to reduce the uncertainty in the estimation of distribution parameters and quantiles. In this paper a regional peaks-over-threshold model is introduced that can be used to analyze precipitation extremes in a changing climate. We use a temporally varying threshold, which is determined by quantile...

Preface “Observing and modeling the catchment scale water cycle”

Convolutional Models for Landmine Identification with Ground Penetrating Radar

This thesis presents new developments in the area of target identification with ground penetrating radar (GPR) intended for the identification of plastic and metal cased antipersonnel (AP) landmines from a single measured GPR return signal, called A-scan. The target identification is formulated as a deconvolution problem. As such, the measured...