A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper presents a new port-Hamiltonian model for vehicle manipulators.
A novel controller for wheeled robots handles joystick inputs for smooth steering.
CARML uses meta-learning to avoid obstacles in 2D vehicle navigation.
Motion planning and control are key problems in a collection of robotic applications including the design of autonomous agile vehicles and of minimalist manipulators. These problems can be accurately formalized within the language of affine connections and of geometric control theory. In this paper we overview recent r…
Automated testing framework finds weaknesses in deep control policies.
Configuration spaces of distinct labeled points on the plane are of practical relevance in designing safe control schemes for Automated Guided Vehicles (robots) in industrial settings. In this announcement, we consider the problem of the construction and classification of configuration spaces for graphs. Topological da…
Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason a…
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, smal…
The study uses Gaussian mixture models to estimate pipe wall thickness from partial scans.
This work uses QPGPs to improve ILC performance in repetitive tasks.
Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quaranti…
Triple-GAIL learns from multiple sources to improve imitation learning for complex behaviors.
The paper introduces metrics for robust unsupervised learning of vehicle interactions.
In the automation of many kinds of processes, the observable outcome can often be described as the combined effect of an entire sequence of actions, or controls, applied throughout its execution. In these cases, strategies to optimise control policies for individual stages of the process might not be applicable, and in…
Deep learning tool classifies urban delivery vehicles.
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the liveli…
A new algorithm finds optimal solutions for constrained decision processes.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
This paper proposes IMU preintegrated features for efficient deep inertial odometry.
In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while th…
Proposes a privacy-preserving system for federated learning of road networks.
Generative model predicts vehicle faults up to 1000 hours in advance.
Generative model learns vehicle trajectory distributions for better data generalization.
Proposes a method to model multi-vehicle interactions using Gaussian processes.
Proposes CTSDG model for better vehicle intention prediction across domains.
In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Mo…
The paper reviews machine learning safety techniques for autonomous vehicles.
New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.
Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicl…
This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial neural networks are trained to compute vehicle controls corresponding to a reference…
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the probl…
This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed alo…
Improved AST method finds more useful failure scenarios for autonomous vehicles.
A novel observer-based method detects and recovers anomalies in CAV sensor readings.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
This paper compares machine learning methods for recognizing lane change intentions from vehicle trajectories.
A tree-based IDS detects cyber-attacks in AV networks.
An active area of research is to increase the safety of self-driving vehicles. Although safety cannot be guarenteed completely, the capability of a vehicle to predict the future trajectories of its surrounding vehicles could help ensure this notion of safety to a greater deal. We cast the trajectory forecast problem in…
Deep learning predicts vehicle behavior for safer autonomous driving.
This work improves vehicle trajectory prediction for safer self-driving cars.
Deep learning improves vehicle control performance and generalizes well.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
Tackles bridging machine learning and control theory for safety-critical systems.
Neural LNS improves vehicle routing performance.
Deep Q-learning optimizes same-day delivery with vehicles and drones.
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…
This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planni…