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Found 21 papers, 5 papers with code
Co-investment with Payoff Sharing Benefit Operators and Users in Network Design
In this paper, we consider the question of what cooperative mechanisms can benefit both operators and users simultaneously.
A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness
Autonomous Vehicles (AVs) promise a range of societal advantages, including broader access to mobility, reduced road accidents, and enhanced transportation efficiency.
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes
Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique.
Factorization of Multi-Agent Sampling-Based Motion Planning
Although standard Sampling-based Algorithms (SBAs) can be used to search for solutions in the robots' joint space, this approach quickly becomes computationally intractable as the number of agents increases.
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games
We consider the interaction among agents engaging in a driving task and we model it as general-sum game.
CARMA: Fair and efficient bottleneck congestion management via non-tradable karma credits
In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion.
A self-contained karma economy for the dynamic allocation of common resources
This paper presents karma mechanisms, a novel approach to the repeated allocation of a scarce resource among competing agents over an infinite time.
Task-driven Modular Co-design of Vehicle Control Systems
When designing autonomous systems, we need to consider multiple trade-offs at various abstraction levels, and the choices of single (hardware and software) components need to be studied jointly.
Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach
The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a-priori guaranteed confidence on the data-driven estimation.
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning
In particular, we show that, when using weight decay, weights in successive layers of a deep network may become "mutually frozen".
On Assessing the Usefulness of Proxy Domains for Developing and Evaluating Embodied Agents
We propose the relative predictive PU to assess the predictive ability of a proxy domain and the learning PU to quantify the usefulness of a proxy as a tool to generate learning data.
The Negative Pretraining Effect in Sequential Deep Learning and Three Ways to Fix It
Negative pretraining is a prominent sequential learning effect of neural networks where a pretrained model obtains a worse generalization performance than a model that is trained from scratch when either are trained on a target task.
Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
In this work, we consider the problem of co-designing the control algorithm as well as the platform around it.
Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents
As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible.
Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions
This requires tools to study such a coupling and co-design mobility systems in terms of different objectives.
A Compositional Sheaf-Theoretic Framework for Event-Based Systems (Extended Version)
A compositional sheaf-theoretic framework for the modeling of complex event-based systems is presented.
Quantifying the effect of representations on task complexity
To quantify this alignment effect of data representations on the difficulty of a learning task, we make use of an existing task complexity score and show its connection to the representation-dependent information coding length of the input.
The Frechet Distance of training and test distribution predicts the generalization gap
Empirically across several data domains, we substantiate this viewpoint by showing that test performance correlates strongly with the distance in data distributions between training and test set.
Today Me, Tomorrow Thee: Efficient Resource Allocation in Competitive Settings using Karma Games
We present a new type of coordination mechanism among multiple agents for the allocation of a finite resource, such as the allocation of time slots for passing an intersection.
Event-based Vision: A Survey
Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur.
Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks
We define a "rulebook" as a pre-ordered set of "rules", each akin to a violation metric on the possible outcomes ("realizations").
