Enhances KWS in vehicles with multi-source fusion.
problem Improving precision and recall rates in vehicle keyword spotting.
method Integrates vehicle information into a DNN for speech classification and selects optimal sensitivity parameters.
result Significantly improved performance metrics (precision, recall, MSE) compared to baseline.
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.
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.
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.
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…
CitySim dataset captures vehicle trajectories for safety research.
problem Lack of fine-grain vehicle trajectories for safety-oriented research.
method Five-step procedure: video stabilization, object filtering, stitching, detection, and error filtering.
result CitySim dataset improves safety evaluations and facilitates digital-twin research.
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.
We propose a method for recognizing moving vehicles, using data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an individual data …
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.
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.
A framework for reinforcement learning tackles CVRP with competitive results.
problem Optimizing routes for vehicles with limited capacity.
method Formulates action selection as a mixed-integer optimization problem, uses policy iteration to improve policies.
result Achieves an average gap of 1.7% with state-of-the-art OR methods on CVRP instances.
Study improves queue length estimation from connected vehicles by filtering parameters.
problem Large errors in estimated queue lengths at low market penetration rates.
method Used Kalman and Particle filters as multilevel real-time estimators.
result Filters reduce estimation errors and improve accuracy within 15 minutes.
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.
Paper classifies pedestrians and vehicles detected by LiDAR.
problem Classifying objects from LiDAR data for self-driving cars.
method Used LiDAR-based object detector and Neural Networks classifier.
result Real-time object detection for self-driving vehicles.
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.
This paper compares uncertainty estimation methods for deep learning in autonomous vehicles.
problem Ensuring safety in autonomous vehicles through accurate uncertainty quantification in deep learning models.
method A comparative survey of uncertainty quantification methods in deep neural networks.
result Different methods for uncertainty quantification in DNNs have advantages and downsides for specific AV tasks and types of uncertainty.
Deep neural network reconstructs traffic speeds from sparse vehicle data.
problem Reconstructing traffic speeds from limited probe vehicle data.
method Convolutional neural network architecture for spatio-temporal learning.
result The method can reconstruct traffic speeds with low probe vehicle penetration.
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…
The paper classifies vehicle shapes and colors using deep neural networks.
problem Vehicle reidentification and classification challenges.
method Used deeper neural networks for classification accuracy.
result Good classification accuracy on make/model and color.
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.
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.
The ability to track a moving vehicle is of crucial importance in numerous applications. The task has often been approached by the importance sampling technique of particle filters due to its ability to model non-linear and non-Gaussian dynamics, of which a vehicle travelling on a road network is a good example. Partic…
Enhances nighttime vehicle detection using style transfer and augmentation.
problem Nighttime object detection challenges due to lack of lighting and glare.
method Day-to-night style transfer and labeling-free augmentation with CARLA synthetic data.
result Significant improvements in nighttime vehicle detection with YOLO11 model.
This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural …
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.
In this paper, we first give the regular point reduction and the two types of Hamilton-Jacobi equation for a regular controlled Hamiltonian (RCH) system with symmetry and momentum map on the generalization of a semidirect product Lie group. Next, as an application of the theoretical results, we consider the underwater …
Self-driving vehicles improve safety by predicting surrounding vehicles' trajectories.
problem Ensuring safety of self-driving vehicles through better trajectory prediction.
method Developed a Convolutional Neural Network to forecast vehicle trajectories from raw data.
result Improvement over baseline models in trajectory forecasting accuracy.
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.
TensorFI injects faults in TensorFlow programs to assess their reliability.
problem Ensuring reliability of machine learning systems in safety-critical domains.
method TensorFI is a flexible fault injection framework for TensorFlow applications.
result TensorFI evaluates the resilience of 12 ML programs, including autonomous vehicle DNNs.
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.
DPDP combines neural heuristics with DP for vehicle routing problems.
problem Vehicle routing problems with large scale.
method Deep Policy Dynamic Programming (DPDP) that uses a neural network policy to prioritize and restrict the DP state space.
result DPDP improves upon classical DP algorithms and outperforms neural approaches for TSP, VRP, and TSPTW.
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.
Adaptive stress testing for autonomous vehicles identifies failure scenarios using reinforcement learning.
problem Identifying potential failure scenarios in autonomous vehicle decision-making systems.
method Formulated as a Markov decision process, used reinforcement learning (DRL) to find likely failure scenarios.
result Deep Reinforcement Learning (DRL) finds more likely failure scenarios with fewer simulator calls than Monte Carlo Tree Search (MCTS).
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.
A method predicts driving intentions of human-driven vehicles for safer autonomous driving.
problem Predicting timely driving intentions of human-driven vehicles for autonomous vehicles in mixed traffic.
method A Hidden Markov Model (HMM) approach using continuous mobility features.
result HMMs trained with continuous mobility features improve prediction accuracy.
MC-pix2pix generates high-quality synthetic sonar data for ATR systems.
problem Generating realistic synthetic sonar data for ATR systems.
method Markov Conditional pix2pix (MC-pix2pix) method.
result MC-pix2pix-generated data is almost indistinguishable from real sonar data.
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.
Study on implied certainty equivalent rates in financial markets and electric vehicles.
problem Investment risk in financial markets.
method Mathematical derivation of implied certainty equivalent rate, empirical analysis of stock and option data.
result Positive implied certainty equivalent rates are more suitable for investment than negative ones, but higher values increase risk.
Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like …
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 tackles security challenges in CAVs using ML, proposing defenses against adversarial attacks.
problem Security threats posed by adversarial machine learning in CAVs.
method An in-depth analysis of ML pipeline in CAVs, identifying security issues and proposing defenses.
result Proposes solutions to defend against adversarial attacks in CAVs.
An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: ran…
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.