arXiv research

Optimal vehicle repositioning policy found for shared mobility services.

This paper improves transportation efficiency by teaching automated vehicles to cooperate.

Federated LIDAR aided beam selection reduces mmWave beam search overhead.

Study shows insurance industry in North Macedonia declined 10% due to COVID-19.

Safe-M$^3$-UCRL learns safe policies for multi-agent systems with global constraints.

Deep RL mimics human driving for collision avoidance in self-driving cars.

Predicts destinations and routes from partial trajectory data.

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.

Study uses trajectory embedding to measure place function similarity at fine spatial granularity.

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…

New model improves multimodal autoencoders by learning joint and conditional distributions.

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…

Optimizes UUV hull design with a two-orders-of-magnitude speedup.

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…

Study improves semantic segmentation of LiDAR point clouds for autonomous vehicles.

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…

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.

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.

An accurate road surface friction prediction algorithm can enable intelligent transportation systems to share timely road surface condition to the public for increasing the safety of the road users. Previously, scholars developed multiple prediction models for forecasting road surface conditions using historical data. …

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…

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to diffe…

New machine learning pipeline solves dynamic vehicle routing problems efficiently.

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…

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.

Sym-NCO leverages symmetricities to improve DRL-NCO performance.

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…

This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaster monitoring. Such applications involve a high demand for high-throughput data transmissions such as real-time video-, image-, and voice- st…

New system assigns vehicles to routes for cost and energy efficiency.

In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor reading…

CitySim dataset captures vehicle trajectories for safety research.

Bayesian model for energy consumption helps electric vehicles navigate efficiently.