Detects ridesourcing cars from ordinary vehicles using transfer learning.
problem Unauthorized ridesourcing activities impairing the industry.
method Two-stage transfer learning framework: RF for initial labeling, CNN for final detection.
result Achieves similar accuracy to supervised learning methods without labeled 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.
Project accelerates pedestrian detection in self-driving cars using FPGA.
problem Improve pedestrian detection accuracy in self-driving vehicles.
method Training and tuning SSD, FPGA design for acceleration.
result 4x performance improvement with minimal accuracy loss.
Study tackles imbalanced data in car insurance claims prediction.
problem Predicting rare events (claims) in car insurance with imbalanced data.
method Various machine learning techniques (logistic-regression, decision tree, random forest, xgBoost, feed-forward network) applied to imbalanced dataset.
result Comparison of machine learning algorithms' performance in claim occurrence prediction.
Proposes novel method for detecting novel scenarios in autonomous systems.
problem Detecting when a machine learning model makes a trustworthy prediction in dynamic, real-world situations.
method Leverages trained model's learned information and a new image similarity metric.
result Demonstrates the method's efficacy on real-world driving and indoor racing datasets.
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.
Paper tackles domain adaptation in object detection, improving accuracy.
problem Real-world object detection faces domain shift issues.
method Formulates domain adaptation as noisy label training; uses noisy bounding boxes from source domain.
result Significantly improves object detection accuracy on various domain adaptation scenarios.
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.
Deep learning and prior maps improve traffic light recognition for autonomous cars.
problem Recognizing traffic lights for autonomous cars in urban environments.
method Combining deep learning-based detection with prior maps for traffic light identification and state recognition.
result The proposed system correctly identified relevant traffic lights along predefined routes.
SELD-TCN improves sound event localization and detection efficiency.
problem Efficient sound event localization and detection on embedded hardware.
method Developed a novel temporal convolutional network (TCN) architecture.
result SELD-TCN outperforms state-of-the-art SELDnet on four datasets.
New approach detects small defects on car surfaces with high accuracy.
problem Automated detection of small defects on specular car surfaces.
method Spline smoothing for feature extraction and k-nearest neighbour classifier.
result Near zero misclassification error rate achieved with standard learning classifiers.
Efficiently detects failures in autonomous systems using adaptive stress testing.
problem Identifying likely failure scenarios in autonomous systems.
method Employed a recurrent neural network to simulate initial conditions and detect failures.
result Solver can now find solutions for previously intractable problems.
Analyze and predict complex 3D shape deformations using LSTM autoencoders and oriented bounding boxes.
problem Detecting and predicting patterns in sequences of deforming 3D shapes.
method Use LSTM autoencoders to create low-dimensional representations of 3D shapes, incorporating oriented bounding boxes for structural components.
result The method detects patterns in plastic deformation and predicts future states of 3D shapes with improved accuracy.
Flexible pipeline for 3D vehicle detection from 2D images.
problem Current methods lack 3D perception of vehicles and other objects.
method Adopt any 2D detection network, fuse with 3D point cloud, develop model fitting algorithm, refine with CNN.
result 3D detection results rank second among algorithms, demonstrating competencies.
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.
Proposes a mathematical model for safe and scalable self-driving cars.
problem Ensuring safety and scalability in self-driving cars.
method Introduces Responsibility-Sensitive Safety (RSS) model and scalable design.
result Proposes a mathematical model for safety assurance and scalable design.
A new car following model reduces platoon level traffic errors.
problem Enhance traffic micro-simulation accuracy in platoon level traffic.
method Unidirectional interconnected LSTM model structure and scheduled sampling technique.
result The proposed model reduces errors by almost 40% compared to traditional models.
Detects adversarial samples using density ratio estimation.
problem Adversarial samples lead to incorrect classifications in machine learning models.
method Direct density ratio estimation for model agnostic detection of adversarial samples.
result Effective detection of adversarial samples with various methods and constraints.
A car's motion shows flat parabolic geometry.
problem Understanding the geometry of a car's motion.
method Analyzing a car as a nonholonomic system.
result Shows a car's motion as a flat parabolic geometry.
A kinematics of the motion of a car is reformulated in terms of the theory of gauge potentials (connection on principal bundle). E(2)-connection originates in the no-slipping contact of the car with a road.
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.
Predicting car sales using maximum entropy principle and proportional growth dynamics.
problem Predicting the sales of new cars over time.
method Analyzing 10 years of Spanish car sales data, applying Gibrat's law, and using the Maximum Entropy Principle.
result Car sales distribution follows predictions from the Maximum Entropy Principle for proportional growth systems.
Reduces car control labels to simplify autonomous driving.
problem Redundant labels in semantic maps hinder efficient autonomous driving.
method Quantifies label importance for car control, simplifies labels.
result Reduced labels improve efficiency and simplify autonomous driving tasks.
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.
DAD++ improves test-time adversarial defense without access to training data.
problem Improving adversarial robustness of pre-trained models without access to training data.
method Test-time Data-free Adversarial Defense (DAD++) with detection and correction schemes.
result DAD++ significantly improves adversarial robustness with minimal clean accuracy drop.
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.
New security patch for object tracking in self-driving cars mitigates vulnerabilities.
problem Vulnerability of object tracking in self-driving cars, especially Kalman Filter-based trackers.
method Adaptive strategy to balance focus on observations and predictions based on deviation anomaly index.
result Certified effectiveness against a generalized hijacking attack model.
Proposes a method for training Bayesian neural networks using synthetic data from Raman and CARS spectra.
problem Limited real observations in Raman and CARS spectroscopy.
method Log-Gaussian Gamma Processes and Bayesian Neural Networks.
result Trained Bayesian neural networks provide accurate estimates of Raman and CARS spectra with uncertainty quantification.
Paper uses FMCW radar and FCN for object detection and 3D estimation.
problem Object detection and 3D estimation using FMCW radar.
method Employed deep learning (FCN) over traditional signal processing. Normalization method applied to radar signal.
result System successfully detects and estimates 3D position of objects in noisy environments.
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.
Framework fuses RGB images and depth maps for self-driving car control.
problem Fault tolerance in self-driving cars with sensor failures.
method Deep neural network architecture for sensor fusion.
result Framework can learn to use relevant sensor information even when one fails.
TFCheck detects training issues in ML programs using TensorFlow.
problem Training programs often contain inconsistencies and bugs.
method Developed a TensorFlow library (TFCheck) with verification routines.
result TFCheck successfully detects training issues in ML code.
Improved car-hailing service by analyzing POI effects.
problem Minimize passenger waiting time and optimize vehicle utilization.
method Analyzed POI effects on supply-demand gap and proposed a POI selection scheme integrated with XGBoost.
result More accurate and stable estimation results.
RankNet forecasts car racing positions with improved accuracy and stability.
problem Forecasting rank positions in car racing, especially considering pit stops.
method Cause-effect decomposition in RankNet, incorporating probabilistic forecasting.
result RankNet outperforms baselines significantly, improving MAE by over 10%.
The paper detects adversarial examples in LECs for regression in CPS using variational autoencoder.
problem Detecting adversarial examples in learning-enabled cyber-physical systems (CPS).
method Inductive conformal prediction using a variational autoencoder regression model.
result The method effectively detects adversarial examples with a short delay in an emergency braking system simulation.
Reinforcement learning controls car speed for safe, efficient, and comfortable driving.
problem Safe, efficient, and comfortable car following during autonomous driving.
method Deep reinforcement learning with a reward function for safety, efficiency, and comfort.
result The model reduces dangerous minimum time to collision to 8% of human drivers and maintains efficient headways.
Bayesian approach improves car-following model calibration and validation.
problem Insufficient data and computational constraints limit accurate model calibration.
method Bayesian machine learning and probabilistic programming.
result Unique parameter sets estimated for each driver, outperforming standard approaches.
The paper improves car leasing pricing by forecasting residual values with asymmetric cost functions.
problem Forecasting residual values for leasing contracts with asymmetric cost functions.
method Develops forecasting models with asymmetric cost functions to address the asymmetric costs of forecast errors.
result Forecasting with asymmetric cost functions reduces decision costs by about 8% compared to standard models.
CDDN tackles visual relationship detection with context-dependent diffusion networks.
problem Combustion of combinatorial explosion in relation triplets detection.
method CDDN framework using semantic and visual scene graphs for adaptive information aggregation.
result CDDN achieves state-of-the-art performance on visual relationship detection datasets.
Analyzes Twitter users' opinions on self-driving cars.
problem Understanding public perception of self-driving cars.
method Annotated Twitter dataset, topic modeling, sentiment classification using Twitter features.
result People are generally optimistic but also concerned about self-driving cars.
PointPainting fuses lidar and image data for better 3D object detection.
problem Lidar-only methods outperform fusion methods on 3D object detection benchmarks.
method Sequential fusion by projecting lidar points into image segmentation output and appending class scores.
result Significant improvements on state-of-the-art 3D object detection methods on KITTI and nuScenes datasets.
New method detects adversarial samples in deep neural networks.
problem Adversarial samples can fool deep neural networks, posing a security risk.
method Designing a statistical adversary detection algorithm called nMutant.
result nMutant effectively detects most adversarial samples.
Paper tackles drowsy driving by learning from weakly labeled car acceleration data.
problem Lack of labeled data for estimating driver drowsiness.
method Weakly supervised learning, scalable stochastic optimization.
result Algorithm learns from weakly labeled data, outperforming baseline methods.
Adversarial attacks can fool self-driving cars, changing steering predictions.
problem Security of deep neural networks in self-driving cars.
method Demonstrated adversarial attacks on steering angle prediction model.
result Small image modifications can mislead self-driving car predictions.
Real-time detection of out-of-distribution data in CPS control systems.
problem Detecting out-of-distribution data in CPS control systems for safety.
method Inductive conformal prediction and anomaly detection using variational autoencoders and deep support vector data description.
result Efficient real-time detection with low false alarm rates and comparable execution time.
Method verifies if observed data fits Lévy-Driven Ornstein-Uhlenbeck process.
problem Verifying if observed data fits Lévy-Driven Ornstein-Uhlenbeck process.
method Estimating parameters and approximating the driving process to test CAR(1) Lévy-driven hypothesis.
result Demonstrates method's effectiveness through simulations and real data examples.
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.
GPLVMF improves CARS performance by addressing overfitting and context importance.
problem Overfitting and lack of automatic context importance determination in GP-based CARS.
method GPLVMF applies a non-zero mean function and real-valued latent space to improve GP model performance.
result Significant improvement in performance on real datasets and automatic context importance determination.