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Deep Hybrid Attention Framework for Road Crash Emergency Response Management

Kashifi, M.T.K. (author)

Road traffic crash is a global tragedy that leads to economic loss, injury, and fatalities. Understanding the severity of a road crash at the early stages is vital to timely providing emergency medical services to crash victims. This study developed a crash emergency response management framework that requires basic crash information for...

journal article 2024

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An Assessment of Predictive Models for Operational Management of a Reservoir in a Data-Scarce Basin: A Case Study of the Black Volta Basin

Kasteel, Max (author)

The Bui Dam, the second-largest hydropower dam in Ghana, plays a significant role in the sustainable energy mix of the country. It is managed by the Bui Power Authority (BPA) and has a capacity of hydro-clean generation of 404MW, contributing to 17% of the country’s total electricity generation. However, decision-making at the dam lacks the use...

master thesis 2023

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Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Xin, Jianbin (author), Wu, Xuwen (author), D'Ariano, Andrea (author), Negenborn, R.R. (author), Zhang, Fangfang (author)

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed...

journal article 2023

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Using psychological characteristics of situations for social situation comprehension in support agents

Kola, I. (author), Jonker, C.M. (author), Riemsdijk, M. Birna van (author)

Support agents that help users in their daily lives need to take into account not only the user’s characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However,...

journal article 2023

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Dynamic mathematical models of theory of mind for socially assistive robots

Marão Patrício, M.L. (author), Jamshidnejad, A. (author)

Interactive machines should establish and maintain meaningful social interactions with humans. Thus, they need to understand and predict the mental states and actions of humans. Based on Theory of Mind (ToM), in order to understand and interact with each other, humans develop cognitive models of one another. Our main goal is to provide a...

journal article 2023

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Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments

Zattoni Scroccaro, P. (author), Sharifi K., Arman (author), Mohajerin Esfahani, P. (author)

In the past few years, online convex optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this article, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics,...

journal article 2023

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Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Schumann, J.F. (author), Kober, J. (author), Zgonnikov, A. (author)

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is...

journal article 2023

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Online Caching with no Regret: Optimistic Learning via Recommendations

Mhaisen, N. (author), Iosifidis, George (author), Leith, Douglas (author)

The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem through the lens of optimistic online learning. We build upon the Follow-the...

journal article 2023

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

Roth, M. (author), Stapel, J.C.J. (author), Happee, R. (author), Gavrila, D. (author)

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...

journal article 2022

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Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow

He, Yuxin (author), Li, L. (author), Zhu, X. (author), Tsui, Kwok Leung (author)

Short-term forecasting of passenger flow is critical for transit management and crowd regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven by other latent factors, and exogenous factors bring challenges to the short-term forecasts of passenger flow of urban rail transit networks. An innovative deep...

journal article 2022

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Hierarchical Optimal Maneuver Planning and Trajectory Control at On-Ramps With Multiple Mainstream Lanes

Chen, N. (author), van Arem, B. (author), Wang, M. (author)

Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches in cooperative merging either build on heuristics solutions or prohibit mainline CAVs to change lane on multilane highways. This paper proposes a hierarchical cooperative...

journal article 2022

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Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Li, D. (author), De Schutter, B.H.K. (author)

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the...

journal article 2022

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Curve Tilting With Nonlinear Model Predictive Control for Enhancing Motion Comfort

Zheng, Y. (author), Shyrokau, B. (author), Keviczky, T. (author), Sakka, Monzer Al (author), Dhaens, Miguel (author)

The benefits of automated driving can only be fully realized if the occupants are protected from motion sickness. Active suspensions hold the potential to raise the comfort level in automated passenger vehicles by enabling new functionalities in chassis control. One example is to actively lean the vehicle body toward the center of the corner...

journal article 2022

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Probabilistic Risk Metric for Highway Driving Leveraging Multi-Modal Trajectory Predictions

Wang, X. (author), Alonso-Mora, J. (author), Wang, M. (author)

Road traffic safety has attracted increasing research attention, in particular in the current transition from human-driven vehicles to autonomous vehicles. Surrogate measures of safety are widely used to assess traffic safety but they typically ignore motion uncertainties and are inflexible in dealing with two-dimensional motion. Meanwhile,...

journal article 2022

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Using Eye-Tracking Data to Predict Situation Awareness in Real Time During Takeover Transitions in Conditionally Automated Driving

Zhou, Feng (author), Yang, X. Jessie (author), de Winter, J.C.F. (author)

Situation awareness (SA) is critical to improving takeover performance during the transition period from automated driving to manual driving. Although many studies measured SA during or after the driving task, few studies have attempted to predict SA in real time in automated driving. In this work, we propose to predict SA during the takeover...

journal article 2022

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Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network

Tao, Qinghua (author), Li, Zhen (author), Xu, Jun (author), Lin, Shu (author), De Schutter, B.H.K. (author), Suykens, Johan A.K. (author)

Traffic flow (TF) prediction is an important and yet a challenging task in transportation systems, since the TF involves high nonlinearities and is affected by many elements. Recently, neural networks have attracted much attention for TF prediction, but they are commonly black boxes with complex architectures and difficult to be interpreted,...

journal article 2022

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Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways

Wang, X. (author), Li, Z. (author), Alonso-Mora, J. (author), Wang, M. (author)

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We...

conference paper 2022

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Regulations Aware Motion Planning for Autonomous Surface Vessels in Urban Canals

de Vries, J.M. (author), Trevisan, E. (author), van der Toorn, J. (author), Das, T. (author), Ferreira de Brito, B.F. (author), Alonso-Mora, J. (author)

In unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Our method builds upon local model predictive...

conference paper 2022

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Improving Pedestrian Prediction Models with Self-Supervised Continual Learning

Knödler, L. (author), Salmi, C. (author), Zhu, H. (author), Ferreira de Brito, B.F. (author), Alonso-Mora, J. (author)

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In...

journal article 2022

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A Switching Thrust Tracking Controller for Load Constrained Wind Turbines

Gonzalez Silva, J. (author), van der Hoek, D.C. (author), Mulders, S.P. (author), Ferrari, Riccardo M.G. (author), van Wingerden, J.W. (author)

Wind turbines are prone to structural degradation, particularly in offshore locations. Based on the structural health condition of the tower, power de-rating strategies can be used to reduce structural loads at the cost of power losses. This paper introduces a novel closed-loop switching control architecture to constrain the thrust in individual...

conference paper 2022