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.
CARML uses meta-learning to avoid obstacles in 2D vehicle navigation.
Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D d…
SECRM-2D improves RL-based autonomous driving with safety guarantees.
DFKI Cabin Simulator tests visual monitoring functions in vehicles.
For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to de…
UAVs use RL to outperform a greedy shooter in 2D engagements.
Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theore…
Algorithm speeds up search for stationary targets with guaranteed accuracy.
Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning measures. Class…
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
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 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.
FedGAN trains GANs across distributed data sources with reduced communication.
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…
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.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
Deep learning improves vehicle control performance and generalizes well.
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…
New approach estimates vehicle and component prices without teardowns.
Autonomous vehicles are expected to navigate in complex traffic scenarios with multiple surrounding vehicles. The correlations between road users vary over time, the degree of which, in theory, could be infinitely large, thus posing a great challenge in modeling and predicting the driving environment. In this paper, we…
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distribution…
Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we t…
This research predicts vehicle movements by analyzing their intentions relative to road lanes.
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of unc…
Deep neural features identify unique vehicles from dash-cam feeds.
Safe reinforcement learning for autonomous vehicles using prediction constraints.
This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is prop…
New system assigns vehicles to routes for cost and energy efficiency.
Automated vehicles learn to predict upcoming maneuvers with high accuracy.
CitySim dataset captures vehicle trajectories for safety research.