Acoustic sensors identify vehicles using spectral embedding.
problem Vehicle recognition from roadside audio sensors.
method Extract frequency signatures, apply spectral embedding for dimensionality reduction.
result K-nearest neighbors achieve accurate vehicle identification after dimensionality reduction.
Drive2Vec embeds vehicle sensor data into a compact low-dimensional form.
problem Capturing vehicle state from sensor data for predictive tasks.
method Stacked gated recurrent units (GRUs) for multiscale state-space embedding.
result Outperforms other methods by up to 90% in various predictive tasks.
Automatically identifies vehicles from audio sensors without needing labeled data.
problem Vehicle recognition and classification from acoustic signals.
method Incremental reseeding of acoustic signatures using spectral embedding and clustering.
result Incremental reseeding accurately identifies individual vehicles from their acoustic signatures.
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.
nuScenes dataset includes multimodal sensor data for autonomous vehicle training.
problem Training robust detection and tracking methods for autonomous vehicles.
method Presented the first multimodal dataset with 6 cameras, 5 radars, and 1 lidar, 360-degree field of view.
result 7x more annotations and 100x more images than KITTI dataset.
Automatically builds a vehicle passage classifier using LSTM-RNNs.
problem Vehicle passage detection using complex sensor data.
method Automatic construction of a binary classifier based on LSTM-RNNs.
result Demonstrated that automatic RNN training can replace handcrafted classifiers.
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.
This paper designs sensor arrays for estimating unsteady flows efficiently.
problem Estimating high-dimensional unsteady flow fields with limited sensor placement.
method Combines data-driven modeling, Kalman Filter design, and sparsification for sensor selection.
result Proposed sensor arrays are highly effective for flow-field estimation across various conditions.
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.
The paper improves safety in autonomous systems using adversarial learning.
problem Ensuring safety in real-time control systems of autonomous vehicles.
method The paper introduces a dual anomaly detection framework (CFAM and SFAM) using generative adversarial networks (GANs) and video prediction.
result Demonstrated effectiveness on both indoor and outdoor autonomous ground vehicles.
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.
Cooperative perception improves 3D object detection in autonomous vehicles.
problem Limited field-of-view and occlusion in single sensor data.
method Early fusion of point clouds from multiple sensors, late fusion of independently detected bounding boxes, and hybrid combination.
result Early fusion approach outperforms late fusion by significantly higher recall (95%) compared to single-point sensing (30%).
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.
CARML uses meta-learning to avoid obstacles in 2D vehicle navigation.
problem Collision avoidance in 2D vehicle navigation.
method Model-Agnostic Meta-Learning for multi-objective reinforcement learning.
result CARML outperforms a baseline TD3 solution in obstacle avoidance.
Framework tracks vehicles adaptively with occlusion handling.
problem Accurate tracking of moving objects in autonomous driving with occlusions.
method Modified mixture particle filter with learning-based behavioral models.
result Framework tracks all vehicles simultaneously, handles occlusions effectively.
We apply variational inference to learn vehicle trajectory parameters from noisy data.
problem Learning parameters for vehicle trajectory estimation from noisy measurements.
method Gaussian variational inference with parameter learning in a motion and sensor model context.
result High-quality state estimates achieved even with outliers and false loop closures.
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.
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.
The paper improves traffic volume estimation using neural networks and vehicle probe data.
problem Accurately estimating historical traffic volumes between sparse sensors.
method Combines neural networks with existing profiling method using vehicle probe data.
result Proposed approach yields 24% more accurate estimates than volume profiles.
Federated LIDAR aided beam selection reduces mmWave beam search overhead.
problem Efficient link configuration in mmWave communication systems with reduced beam search overhead.
method Federated learning of LIDAR data to train a shared neural network for beam selection.
result Proposed method significantly outperforms previous works in performance and complexity.
The paper proposes a model to forecast traffic motion from sensor data.
problem Accurately predicting traffic motion for safe vehicle maneuvers.
method Implicit latent variable model using interaction graphs and graph neural networks.
result Achieves state-of-the-art motion forecasting and interaction understanding.
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.
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.
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 paper identifies drivers from a single car turn using sensor data.
problem Predicting driver identity from a single car turn using sensor data.
method Time series classification of sensor readings from a single turn, focusing on unique patterns in each driver's style.
result Accurate identification of drivers from a single turn, even in varied driving conditions.
A CNN for lidar data improves understanding of moving vehicles.
problem Disambiguating the motion of vehicles from a single lidar sensor.
method Proposes a CNN architecture trained with pretext tasks including image data.
result CNN outperforms without image data at test time.
The paper provides guarantees for feedback control with sensor errors.
problem Certifying performance and safety in feedback control systems with sensor errors.
method Solving a supervised learning problem to characterize sensor errors and providing uniform error bounds.
result Finite-time convergence rate on sub-optimality of using a regressor in closed-loop for waypoint tracking.
Study improves prediction accuracy and uncertainty for mobile sensor data using randomized neural networks.
problem Improving prediction accuracy and uncertainty for mobile sensor data.
method Cross-validation and uncertainty determination for randomized neural networks.
result Improved out-of-sample performance and confidence intervals for prediction error.
Modeling driver behavior using GPS data and HDP split-merge sampling.
problem Understanding individual driver behaviors and road network from GPS data.
method Hidden Markov Model (HMM) and Hierarchical Dirichlet Process (HDP) with split-merge sampling.
result Data-driven predictions about destinations and road conditions.
A drone-based MOT algorithm tracks vehicles using neural network detections and TPMBM filter.
problem Tracking multiple vehicles from drone-mounted cameras.
method Neural network for object detection, TPMBM filter for trajectory estimation, von-Mises Fisher distribution for DOA.
result TPMBM filter optimally estimates vehicle trajectories.
DeepLocalization uses neural networks to localize vehicles from landmarks.
problem Vehicle self-localization from multi-modal sensor data and a reference map.
method Deep neural network that regresses vehicle pose from unordered landmarks.
result DeepLocalization achieves state-of-the-art accuracy and is faster than related work.
This research uses PointNets to detect 2D objects from radar data.
problem Detecting 2D objects from sparse radar data for automated driving.
method Adapting PointNets for radar data, performing 2D object classification and bounding box regression.
result Demonstrates the potential of PointNets for 2D object detection in radar data.
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.
DASC combines social media and car sensors to improve disaster response.
problem Inconsistent reliability and inconsistent availability of human sensors.
method Hybrid social-car sensing system using game theory, feedback control, and MDP.
result DASC improves detection accuracy and efficiency in disaster response.
UAVs learn to collect data from IoT sensors efficiently.
problem Efficient UAV path planning for wireless data collection.
method Deep reinforcement learning (DDQN) with experience replay and convolutional layers.
result UAV control policy generalizes over changing scenario parameters.
Deep learning aids UAVs in identifying disasters with high accuracy.
problem Monitoring disasters for effective mitigation.
method Deep learning applied to aerial photos from UAVs.
result 91% accuracy in disaster identification from 544 images.
TensorMap uses tensor decompositions to create accurate topological maps from Lidar data.
problem Creating accurate topological maps from Lidar data in real-time.
method Orthogonal Tucker3 tensor decomposition.
result TensorMap accurately detects the vehicle's position in a graph-based map.
SA-ABR uses UAV sensor data to optimize video streaming quality.
problem Dynamic UAV flight states cause fluctuating video streaming quality.
method SA-ABR integrates sensor data with network observations to train a DRL model.
result SA-ABR outperforms existing ABR algorithms by 21.4% in QoE.
Intelligent vehicles use machine learning to optimize network performance.
problem High dynamics in vehicular environments and large data volumes.
method Machine learning, particularly reinforcement learning, to manage network resources.
result Machine learning can optimize network performance in high mobility vehicular networks.
iDriveSense offers safer trip recommendations by considering road anomalies.
problem Drivers seek safer routes despite shortest/fastest path recommendations.
method Crowdsensing, vehicle sensors, fuzzy systems for road quality assessment.
result Proposes a system for dynamic route planning considering road anomalies.
Proposes LSTM-based method for detecting anomalies in multi-sensor time-series data.
problem Challenges in detecting anomalies in unpredictable time-series data from multiple sensors.
method LSTM-based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD).
result EncDec-AD detects anomalies robustly from various types of time-series data.
Improved UAV navigation and landing using deep learning.
problem Autonomous navigation and landing of UAVs with high accuracy.
method Multimodal fusion of visual and inertial sensor data using deep neural networks.
result 25% improvement in pose estimation accuracy compared to traditional methods.
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.
Industrial vehicle uses LiDAR and camera to detect and avoid restricted areas.
problem Avoiding collisions in industrial settings with automated vehicles.
method Combining LiDAR and camera data, using deep learning for projection, and model-predictive control.
result Reduces false positives in LiDAR detection of reflective beacons.
DFKI Cabin Simulator tests visual monitoring functions in vehicles.
problem Validating novel human-vehicle interfaces and driver assistance systems.
method Driving simulator with in-cabin mock-up and camera system.
result Validation of in-cabin monitoring functions for advanced driver assistance and automated driving.
This study examines LiDAR spoofing attacks on autonomous vehicle perception.
problem Security vulnerabilities in LiDAR-based perception systems in autonomous driving.
method Formulated as an optimization problem, designed input perturbation and objective functions, combined optimization and global sampling.
result Attack success rates improved to around 75% through strategic input perturbation.
Develops a neural network for precise vehicle trajectory prediction.
problem Improving situational awareness in vehicular networks for safety applications.
method Two-layer neural network predicting vehicle parameters and trajectory points.
result Significantly improved prediction accuracy compared to existing methods.
Challenge to separate Earth's magnetic field from vehicle's magnetic field for accurate navigation.
problem Separate Earth's magnetic field from vehicle's magnetic field for accurate magnetic navigation.
method Use machine learning (ML) and integrate physics of magnetic navigation (SciML) to remove aircraft magnetic field from total magnetic field.
result A model can be constructed to effectively remove aircraft magnetic field from the dataset.