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.
AI learns to adapt driving behavior based on human interest.
problem Adapting AI behavior to individual human preferences for trust and comfort.
method Hybrid brain-computer interface (hBCI) detects human interest, which is used to adapt AI behavior in a virtual vehicle.
result AI agent slows down when passengers encounter objects of interest, increasing viewing time for subjects.
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.
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.
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.
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.
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.
This work improves safety validation of autonomous vehicles by finding interpretable failures.
problem Finding interpretable failures of autonomous systems in simulation.
method Signal temporal logic expressions optimized for high likelihood and human interpretability.
result Our methodology finds more interpretable failures with higher likelihood compared to baseline approaches.
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.
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.
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).
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.
Paper improves autonomous vehicle safety and efficiency with new reinforcement learning methods.
problem Improving robustness and safety in autonomous vehicle control.
method Developed and compared two algorithms: Robust Adversarial Reinforcement Learning and Neural Fictitious Self Play.
result The new algorithms lead to improved driving efficiency and reduced collision rates.
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.
Safe RL for autonomous vehicles using PCPO with trust regions and parallel learners.
problem Unexplainable behaviours and lack of safety guarantees in RL for real vehicles.
method PCPO framework with trust regions and parallel learners.
result Safe learning confirmed for autonomous vehicles with fast convergence.
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.
Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.
problem Efficient real-time semantic segmentation for autonomous vehicles.
method Compressed ENet architecture, FPGA deployment, batch processing, filter reduction, quantization-aware training.
result Reduced latency to 3 ms per image with batch size of ten and 40% resource utilization.
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.
Proposes a method to predict vehicle intentions and motion adaptively.
problem Accurately predicting vehicle behaviors in various traffic scenarios.
method Probabilistic framework based on deep neural network.
result Better long-term motion prediction performance.
Deep learning predicts traffic actors' future movements with uncertainty.
problem Predicting future states of traffic actors for autonomous vehicles.
method Deep learning models using raster images of actors' surroundings.
result Effective prediction of traffic actors' movements with uncertainty.
This paper optimizes autonomous vehicle controllers using data-driven methods.
problem Designing robust controllers for autonomous vehicles that handle external and internal disturbances.
method Data-driven approach using principal component analysis and time delay neural networks.
result Improved controller performance through a feed-forward compensator.
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.
IDAS approach for autonomous vehicles to make decisions under merging scenarios.
problem Decision making for autonomous vehicles in merging scenarios with varying driver cooperativeness.
method IDAS approach using multi-agent reinforcement learning (MARL) with curriculum learning and masking mechanism.
result IDAS approach can handle uncertainties in real-world scenarios and make strategic decisions.
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.
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.
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.
The study improves deep learning models for safer autonomous vehicles.
problem Robustness of deep neural network models in autonomous driving.
method Analyzes and proposes solutions for deep learning model robustness.
result Enhanced deep learning models for safer autonomous vehicles.
Improved vehicle motion prediction with uncertainty estimation.
problem Robust motion prediction for autonomous vehicles, especially under distributional shift.
method Presented an approach significantly improving the benchmark and taking 2nd place on the leaderboard.
result Significantly improved motion prediction and uncertainty measurement.
Framework predicts interactions between multiple traffic participants.
problem Accurately predicting interactions between multiple traffic participants.
method Probabilistic framework with hierarchical modules forecasting intentions and motions.
result Framework predicts continuous motions and interaction durations for multiple interacting road participants.
We propose a method to compute optimal control paths for autonomous vehicles deployed for the purpose of inferring a velocity field. In addition to being advected by the flow, the vehicles are able to effect a fixed relative speed with arbitrary control over direction. It is this direction that is used as the basis for…
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.
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.
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.
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.
This paper tackles adversarial robustness of deep neural networks in autonomous vehicles.
problem Proving the correctness of deep neural networks is difficult and existing automated techniques fail to scale.
method Develops techniques to verify adversarial robustness of deep neural networks.
result Demonstrates the feasibility of verifying adversarial robustness of deep neural networks.
Paper proposes an intersection decision algorithm for autonomous vehicles.
problem Navigating intersections with non-automated vehicles.
method Combines reinforcement learning for high-level decisions and model predictive control for low-level planning.
result The proposed algorithm outperforms another controller in success rate and training episodes.
Improved multi-step prediction of drivable space for autonomous vehicles.
problem Accurate prediction of drivable space for safer, more comfortable navigation.
method Recurrent Neural Network (RNN) architectures trained on KITTI dataset, incorporating motion features.
result Significant improvement in prediction accuracy over state-of-the-art methods.
DriveFI uses ML to find critical faults in AVs, saving time and resources.
problem Lack of end-to-end fault assessment in AVs under realistic scenarios.
method Machine learning-based fault injection engine (DriveFI) that identifies safety-critical faults.
result Found 561 safety-critical faults in less than 4 hours, compared to weeks of random injection.
Simulation speeds AV testing by 2-20 times over real-world methods.
problem Lack of scalable and rigorous testing for autonomous vehicles.
method Adaptive importance-sampling methods for rare-event probability evaluation.
result Accelerates accident probability estimation by 2-20 times over naive Monte Carlo methods.
Automated testing framework finds weaknesses in deep control policies.
problem Safety of deep neural network control policies is difficult to validate.
method Adversarial reinforcement learning to test and find weaknesses.
result Framework finds weaknesses not evident during manual testing.
Backdoor attacks on DRL-based traffic controllers cause stop-and-go waves or crashes.
problem Vulnerability of DRL-based traffic controllers to machine learning attacks.
method Developed a trigger design methodology based on traffic physics principles.
result Backdoored models can cause stop-and-go traffic waves or AV crashes when triggered.
Predicts failure of autonomous vehicle steering control models.
problem Evaluating and predicting failure of machine learning models in safety-critical applications.
method Trains a student model to predict the main model's error based on saliency maps.
result Preliminary results show the failure predictor model works on autonomous vehicle steering control systems.
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 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.
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.
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.
Proposes a generic prediction architecture for autonomous vehicles considering both rational and irrational driving behaviors.
problem Accurately predicting future behaviors of surrounding vehicles for safe autonomous vehicle planning.
method Combines learning-based and planning-based models to address rationalities in human behavior.
result Stable prediction performance under various unseen driving scenarios.
A hybrid model combines Q-learning and PID controller for continuous vehicle control.
problem Learning unsatisfactory results with discrete action space in autonomous driving.
method Combining Q-learning and PID controller, using Quadratic Q-function approximation and action network.
result Autonomous vehicle successfully learns smooth and efficient driving behavior.