Advancing Safety in Highway Automated Driving: A Stochastic Model Predictive Approach

This study addresses a critical aspect of automated driving: enhancing safety during highway emergency scenarios. This is achieved by formulating the problem within the framework of Stochastic Model Predictive Control (SMPC). In SMPC, safety constraints are designed such that a small chance of constraint violation is accepted, contrary to...

Autonomous greenhouse climate control with Q-learning using ENMPC as a function approximator

Greenhouses allow production of crops that would otherwise be impossible. Permitting more local, fresher and nutrient richer crop production. Eorts are taken to minimize societal harm due to energy and resource consumption by greenhouse production systems. One way to control such systems is by using model predictive control. Optimal crop yield...

Repositioning in shared mobility systems: Combining model predictive control and approximate dynamic programming

The global market for personal mobility has transformed over the last decade. Traditional taxi services have to compete with the emergence of ride-hailing services such as Uber and Lyft. Rapid developments in available algorithms and real-time inter-connectivity of travellers and vehicles offer mobility-on-demand (MOD) services new possibilities...

Real-Time Predictive Speed Control for Eco-Driving at Signalized Intersections Considering Queue Constraints

Speed trajectories considerably influence vehicular fuel consumption, particularly on signalized roads. To minimize fuel consumption, sharp acceleration/deceleration maneuvers and idling events at signalized intersections should be prevented. By taking advantage of the technological developments in infrastructure-to-vehicle communication, the...

Learning cycling styles using experimental trajectory data with Inverse Reinforcement Learning

Cycling is an increasingly attractive transportation mode, thanks to its health and environmental benefits. Personalized travel assistance services can help make cycling more appealing by providing speed or route advices that can reduce travel time and increase safety while taking into account the personal preferences of cyclists. Due to its...

Multi-criteria route optimisation for electric vehicles on long-haul trips using stochastic dynamic programming

Stochastic Dynamic Programming (SDP) has shown promising results for sequential decision problems of the route optimisation for an Electric Vehicle (EV) with the presence of stochastic variables in the travel cost. However, in studies, the optimisation problem formulation for EVs has been lacking in detail. For example, possible waiting times at...

Practical implementation of reinforcement learning algorithms for giving personalised speed advice to cyclists approaching intersections using function approximation and Dyna

<br/>Being a safe and healthy alternative for polluting and space-inefficient motorised vehicles, cycling can strongly improve living conditions in urban areas. Idling in front of traffic lights is seen as one of the major inconveniences of commuting by bicycle. By giving personalised speed advice, the probability of catching a green light can...

BuBBLeS In Control: A Model-Based-Predictive-Control Strategy For Greenhouse Climate Control With Soap Bubble Cavity

Advancements in knowledge and technology have led to an evolution of greenhouse operations: from simple transparent shelters to (one of) the most profitable sectors in agricultural industry. The understanding of the physiological and biological processes of the inside micro-climate and crop, as well as the introduction of climate control, were...