Motion planning for CAVs in mixed traffic, a study on roundabouts

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Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, tracking, and then forecasting problems, in a multi-step pipeline. In this complex ...

Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

The fatigue provisions for bridge design in North America and Europe are highly simplified for the sake of practical implementation, in comparison with the state of the art in understanding of the true fatigue behavior. Historically, one significant simpli ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

Over-reliance on the automation of transportation systems is understood to be the cause of new types of accidents. In this paper, the automated driving system is augmented with control methods enabling driver intervention without its full deactivation. The ...

The ongoing electrification of the transportation fleet will increase the load on the electric power grid. Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures wil ...

Prediction is a vital component of motion planning for autonomous vehicles (AVs). By reasoning about the possible behavior of other target agents, the ego vehicle (EV) can navigate safely, efficiently, and politely. However, most of the existing work overl ...

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and ...

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Tradition ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Decisions about a current visual stimulus are systematically biased by recently encountered stimuli, a phenomenon known as serial dependence. In human vision, for instance, we tend to report the features of current images as more similar â i.e., an attra ...

Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

A vehicle's steering is a particular system in that it is exposed to individual subjective reviews based on criteria that are hard to assess quantitatively. Haptic design of such systems is a prime concern that has been at the center of industrial developm ...

Microscopic traffic flow models can be distinguished in lane-based or lane-free depending on the degree of lane-discipline. This distinction holds true only if motorcycles are neglected in lane-based traffic. In cities, as opposed to highways, this is an o ...

Motion planning for CAVs in mixed traffic, a study on roundabouts

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Social-Transmotion: Promptable Human Trajectory Prediction

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Reliable data-driven decision-making through optimal transport

Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

Toward Reliable Human Pose Forecasting With Uncertainty

A WIM-Based Assessment of Multiple Vehicle Presence Effects on Fatigue Damage of Highway Bridges

Unveiling the complexity of learning and decision-making

The Haptic Link Enabling Driver-Automation Teaming

GreenEVT: Greensboro Electric Vehicle Testbed

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Challenges of COVID-19 Case Forecasting in the US, 2020-2021

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Serial Dependence in Human Visual Perception and Decision-Making

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Haptics based collaborative steering control in automated driving environments

A hybrid microscopic model for multimodal traffic with empirical observations from aerial footage