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.
On-device research index
arXiv research
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.
169,051 papers · 148 categories
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48 results for “intelligent vehicles”
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.
This paper learns motion primitives from driving data to improve vehicle path-tracking.
problem Improving vehicle path-tracking accuracy through learned motion primitives.
method Two-level structure with path segmentation and clustering; Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR).
result The model predicts future lateral control commands with high accuracy.
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.
This paper surveys DRL for autonomous vehicle motion planning.
problem Designing intelligent motion planning for autonomous vehicles.
method Deep Reinforcement Learning (DRL) for hierarchical motion planning.
result Survey of state-of-the-art DRL solutions for autonomous vehicle motion planning.
As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the hear…
A machine learning environment for detecting autonomous vehicle corner cases.
problem Testing autonomous driving software in the real world is difficult.
method Connecting CARLA simulation software to TensorFlow and custom AI client software.
result The system can identify situations where AI software fails to understand the scenario.
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.
Two prediction models improve supply-demand forecasting for autonomous vehicles.
problem Improving accuracy and stability of supply-demand predictions for autonomous vehicles.
method Two prediction models based on residual network, LSTM, attention mechanism, and multi-attention mechanism.
result Our frameworks provide more accurate and stable prediction results than existing methods.
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…
Novel AI-IMU method accurately estimates vehicle position and orientation.
problem Accurate dead-reckoning for wheeled vehicles using only IMU.
method Kalman filter and deep neural networks for noise adaptation.
result Average 1.10% translational error, competitive with LiDAR or stereo vision methods.
Predicts multiple vehicle trajectories efficiently.
problem Predicting uncertain future motions of agents in dynamic scenes.
method Probabilistic framework learning latent variables for multi-step future modeling.
result State-of-the-art predictions on vehicle trajectory datasets.
Researchers develop PAIN to improve self-driving safety through adversarial training.
problem Overfitting and poor generalizability of neural networks in self-driving vehicles.
method PAIN combines adversarial training in CARLA simulation to generate edge cases.
result Trained self-driving vehicles are more resilient to environmental uncertainty and less prone to collisions.
Survey on modern traffic signal control methods.
problem Minimizing travel time at road intersections.
method Machine learning, reinforcement learning.
result Promotes interdisciplinary research on traffic signal control.
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.
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.
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learningstat.ML
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
problem Improving efficiency and safety of transportation systems with automated vehicles.
method Multi-agent graph reinforcement learning with attention mechanism.
result Automated vehicles can achieve better performance when learning to cooperate with each other.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
Adversarial attacks degrade DRL-based EV energy management systems.
problem Adversarial attacks on DRL-based energy management systems of electric vehicles.
method Generated adversarial examples to degrade DRL performance using low-dimensional state representations.
result Adversarial attacks can significantly degrade DRL-based EV energy management systems.
D-City is a diverse traffic dataset for AI development.
problem Lack of comprehensive, detailed datasets for AI in driving.
method Collects over 10,000 dashcam videos with detailed annotations.
result Advances AI in driving perception and related areas.
Paper develops a neural-fuzzy controller for GPS-intelligent buoys.
problem Optimally track dynamically positioned marine buoys with unknown parameters.
method Dynamic system modeling using neural-fuzzy networks with backstepping technique.
result The controller minimizes position errors and adjusts buoy positions accurately.
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Drivingcs.RO
Framework tracks and predicts multiple objects in autonomous driving.
problem Challenges in multi-target tracking due to object number fluctuation and occlusion.
method Constrained mixture sequential Monte Carlo (CMSMC) method with a mixture representation.
result Framework can track and predict multiple objects simultaneously without explicit data association.
SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensingcs.LG
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 …
A learning-based approach optimizes automated lane changes with mixed-integer optimization and machine learning.
problem Traditional motion planning methods are inefficient and lack generalization capability.
method Mixed-Integer Quadratic Problem (MIQP) for optimal trajectories, supervised learning for fast decision-making.
result The proposed model outperforms existing motion planning methods in optimality, efficiency, and generalization.
Deep models can be fooled by maliciously crafted examples.
problem Deep learning models are vulnerable to adversarial examples that can mislead predictions without being noticed by humans.
method Reviews adversarial example generation methods and defenses, discusses their limitations and future prospects.
result Adversarial examples can mislead deep learning models without being distinguishable by humans.
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
problem Designing efficient underwater vehicle hulls using CFD simulations.
method Bayesian Optimization-LCB algorithm and DNN-based surrogate model.
result Two-orders-of-magnitude speedup in design optimization process.
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.
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.
Game theory improves smart road sign security against small perturbations.
problem Ensuring smart road signs are secure from small-scale adversarial attacks.
method Integrates game theory into smart road sign classification to detect imperceptible perturbations.
result Proposes a randomized detection strategy to ensure robustness against worst-case attackers.
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.
Bayesian model assesses criminal threat escalation.
problem Evaluate threats posed by potential violent criminals.
method Customised three-level Bayesian hierarchical model.
result Model calibrates to expert judgments and updates in real time.
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.
Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the ot…
Deep learning models control vehicle dynamics on a track.
problem Coupled longitudinal and lateral control of a vehicle.
method Trained two neural networks (MLP and CNN) to compute controls based on high-fidelity simulations.
result Deep learning models outperform conventional controllers on a challenging track.
Automobile theft detection method uses owner driving data clustering.
problem Automobile theft detection challenges with limited real data.
method Clustering owner driving data using k-means algorithm, then detecting theft by comparing reconstructed data.
result 99% accuracy in detecting vehicle theft using only owner driving data.
TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learningcs.LG
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 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.
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.
AVs learn from past experiences to improve future performance.
problem Challenging situations and unknown experiences for AVs.
method Transfer Learning and Organic Computing.
result Online Transfer Learning helps update knowledge as tasks evolve.
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.
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic datastat.ML
HGP models traffic speed uncertainty better than current methods.
problem Highly variable measurement noise in crowdsourced traffic data.
method Heteroscedastic Gaussian processes (HGP) conditioned on sample size and traffic regime (SRC-HGP).
result SRC-HGP produces significantly better predictive distributions.
Scalable Autonomous Vehicle Safety Validation through Dynamic Programming and Scene Decompositioncs.RO
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.
Paper automates car negotiation in intersections using Q-learning.
problem Automated vehicles negotiate with human-driven cars in intersections.
method Deep Q-learning applied to simulated traffic with various driver behaviors.
result 98% success rate in avoiding collisions with other vehicles.
Deep RL model mimics human car-following behavior with high accuracy.
problem Developing autonomous cars that can follow other vehicles like humans.
method Deep reinforcement learning using historical driving data and reward function.
result DDPGvRT model achieves lower validation errors (18% spacing, 5% speed) compared to other models.