Paper proposes a deep autoencoder model to detect anomalies in CAV locations.
problem Early detection of anomalies in self-reported vehicle locations for CAVs.
method Unsupervised learning model based on deep autoencoder using vehicle locations and RSSI.
result The proposed model is effective and robust in detecting self-reported location anomalies.
A tree-based IDS detects cyber-attacks in AV networks.
problem Cyber-attacks in AV and IoV networks.
method Tree-structure machine learning models, ensemble learning, feature selection.
result High detection rate and low computational cost achieved.
World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of …
New method identifies drivers from car logs without reverse-engineering CAN protocol.
problem Identifying drivers from in-vehicle network logs without access to exact signal semantics.
method Machine learning techniques applied to off-the-shelf data.
result Driver re-identification accuracy of 75-85% on a dataset of 33 drivers.
Paper proposes personalized climate control for driver comfort.
problem Limited research on in-vehicle climate control and driver preferences.
method IoT platform for data collection, machine learning for driver behavior recognition, and personalized preference recommendation.
result Prototype demonstrates effective and accurate climate control for driver comfort.
Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make…
This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the …
Paper uses t-SNE to classify China's Internet finance risks.
problem Systemic risk characteristics of China's Internet finance during macroeconomic shocks.
method t-SNE machine learning algorithm for data mining and risk classification.
result Identified peak and thick-tail characteristics of Internet financial systemic risk.
Deep learning tool classifies urban delivery vehicles.
problem Counting and categorizing delivery vehicles in cities.
method Developed annotated database and retrained CNNs.
result Accurate classification of 90%+ for 3 vehicle classes.
With the large-scale penetration of the internet, for the first time, humanity has become linked by a single, open, communications platform. Harnessing this fact, we report insights arising from a unified internet activity and location dataset of an unparalleled scope and accuracy drawn from over a trillion (1.5$\times…
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
problem Lack of efficient and adaptable evaluation methods for autonomous vehicles.
method Adaptive evaluation framework using ensemble models and nonparametric Bayesian clustering.
result Adversarial scenarios significantly degrade tested autonomous vehicles' performance.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
problem Lack of infrastructure and pooled vehicle info in existing AV datasets.
method Developed VTrackIt, a synthetic dataset with intelligent infrastructure and pooled vehicle info, and introduced InfraGAN for trajectory predictions.
result VTrackIt reduces high-risk edge cases in AV trajectory predictions.
The study finds that Chinese internet users have different search behaviors and attention patterns.
problem Heterogeneity in search behavior and attention among Chinese internet users.
method Data extraction technology to analyze Baidu Index keyword search volume data.
result Chinese internet users exhibit different search behaviors and attention patterns.
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.
problem Privacy and security of data shared between vehicles and infrastructure.
method Federated learning over V2V and V2N links, non-IID dataset modeling.
result Improves learning performance and prevents eavesdropping.
Generative model predicts vehicle faults up to 1000 hours in advance.
problem Forecasting vehicle faults for predictive maintenance.
method Generative model trained on US Army data, incorporating real-world factors.
result Highly accurate predictions of time to first fault.
Generative model learns vehicle trajectory distributions for better data generalization.
problem Data sparsity and privacy issues in urban vehicle trajectory analysis.
method Generative adversarial imitation learning framework for urban vehicle trajectory generation.
result TrajGAIL model produces synthetic trajectories similar to real ones, achieving significant performance gains.
Proposes a method to model multi-vehicle interactions using Gaussian processes.
problem Challenges in modeling correlations between multiple road users over time.
method Uses a stochastic vector field model and non-parametric Bayesian learning.
result Captures motion patterns from complex multi-vehicle interactions without heroic prior assumptions.
Proposes CTSDG model for better vehicle intention prediction across domains.
problem Domain generalization for vehicle intention prediction in dynamic environments.
method Structural causal model with recurrent latent variable integration.
result Consistent improvement in prediction accuracy compared to state-of-the-art methods.
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.
problem Challenges in machine learning safety for autonomous vehicles.
method Organizes practical safety techniques to complement engineering safety.
result Enhances dependability and safety of machine learning algorithms in autonomous vehicles.
New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.
problem Efficiently discovering rare failure events in autonomous vehicle simulations.
method Approximate dynamic programming and scene decomposition to estimate failure distribution.
result Increased number of failures discovered compared to baseline approaches.
Paper proposes a method to improve autonomous vehicle performance using synthetically generated images.
problem Limited access to real-world datasets for autonomous vehicle training in countries with scarce data.
method Synthetically generated images to augment and train neural networks on small datasets.
result About 10% improvement in model performance observed.
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…
Study predicts internet-based treatment effects for GPPPD based on dyadic coping.
problem Identifying which patients will benefit most from internet-based GPPPD treatment.
method Developed a multivariable decision tree model using recursive partitioning.
result Predicts large effects for high dyadic coping patients, small effects for low dyadic coping patients.
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.
problem Finding useful failure scenarios for autonomous vehicle validation is challenging.
method Adaptive Stress Testing with reward augmentation, modified to encode domain information.
result The modified AST method discovers a larger and more expressive subset of failure scenarios.
A novel observer-based method detects and recovers anomalies in CAV sensor readings.
problem Improving safety and security in connected and automated vehicles.
method Combines model-based signal filtering and anomaly detection methods using AEKF and OCSVM.
result The proposed method achieves better anomaly detection performance compared to traditional methods.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
This paper compares machine learning methods for recognizing lane change intentions from vehicle trajectories.
problem Accurately detecting and predicting lane change processes in autonomous vehicles.
method Comparison of different machine learning methods on high-dimensional time series data.
result Ensemble methods reduce Type II and Type III classification errors, while LightGBM outperforms XGBoost in training efficiency.
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.
problem Enhance autonomous vehicles' hazard awareness in complex environments.
method Review of deep learning-based approaches for vehicle behaviour prediction.
result Deep learning outperforms conventional methods in complex scenarios.
This work improves vehicle trajectory prediction for safer self-driving cars.
problem Improving trajectory predictions for safer self-driving cars.
method Deep-learning-based method combining AI and physically grounded models.
result Proposes a method that outperforms existing state-of-the-art.
Deep learning improves vehicle control performance and generalizes well.
problem Designing a controller for autonomous vehicles in diverse scenarios.
method Application of deep learning methods for vehicle control.
result Deep learning methods provide excellent performance and generalization.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.
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…
Neural LNS improves vehicle routing performance.
problem Optimizing vehicle routes with constraints.
method Integrates deep learning with large neighborhood search.
result Significantly outperforms existing methods on CVRP.
Deep Q-learning optimizes same-day delivery with vehicles and drones.
problem Optimizing same-day delivery with limited vehicle and drone capacities.
method Deep Q-learning approach to assign packages to vehicles or drones.
result Deep Q-learning policy outperforms benchmark policies and maintains effectiveness with changing fleet sizes.
New approach estimates vehicle and component prices without teardowns.
problem Uncertainty in traditional vehicle price estimation methods.
method Data-driven machine learning and game theory.
result Shows large gaps in manufacturer pricing strategies.
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
problem Efficiently handling same day deliveries in e-commerce logistics.
method Combination of machine learning and combinatorial optimization.
result Ranked first in the EURO Meets NeurIPS Vehicle Routing Competition.
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.
problem Accurately forecasting vehicles' future movements for safe autonomous driving.
method LSTM networks with attention mechanisms applied to spatio-temporal graphs of road lanes.
result The model outperforms other state-of-the-art models in several metrics.
Automated vehicles need efficient testing, which this method addresses.
problem Current testing practices are insufficient for automated vehicles.
method Gaussian Process Classification to identify performance boundaries.
result It is feasible to test for challenging scenarios more efficiently.
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.
problem Identifying unique vehicles in dash-cam feeds for self-driving cars.
method Used pretrained YOLO network feature maps to create deep integrated feature signatures (DIFS) for 700 images of 35 vehicles and 340 images of 17 vehicles.
result Correctly identified unique vehicles at 96.7% for high resolution data and 86.8% for lower resolution data.
Safe reinforcement learning for autonomous vehicles using prediction constraints.
problem Safe reinforcement learning for safety-critical applications like autonomous vehicles.
method Use prediction to constrain exploration in reinforcement learning models.
result Successfully learned intersection handling behaviors on an autonomous vehicle.