The autonomous norm on torus Hamiltonian diffeomorphisms is unbounded.
problem Understanding the unbounded nature of the autonomous norm on Hamiltonian diffeomorphisms of the torus.
method Constructing quasimorphisms and showing they are Calabi to prove the unboundedness of the autonomous norm.
result Explicit examples of Hamiltonian diffeomorphisms with arbitrarily large autonomous norm.
Note proves bi-Lipschitz embeddings for Ham(S^2) metrics.
problem Entropy norms on Ham(S^2) bi-Lipschitz embeddings.
method Proves bi-Lipschitz embeddings for Ham(S^2) with entropy metrics.
result Existence of bi-Lipschitz embeddings for Ham(S^2) metrics.
We use the criteria of Lalonde and McDuff to determine a new class of examples of length minimizing paths in the group Ham(M). For a compact symplectic manifold M of dimension two or four, we show that a path in Ham(M), generated by an autonomous Hamiltonian and starting at the identity, which induces no non-cons…
Paper develops rules for autonomous driving using semantic memory.
problem Creating rules for autonomous driving systems.
method Uses real driving data and semantic memory for rule learning.
result Automatic rule learning for autonomous driving.
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.
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.
A novel approach learns constraints and maximizes rewards for autonomous agents.
problem Ensuring autonomous agents align with societal norms and values.
method Inverse reinforcement learning for constraints, contextual bandit orchestrator for policy mixing.
result Agent learns to act optimally within constraints and maximize rewards.
Proves rigidity of 3D partially hyperbolic systems via autonomous dynamics.
problem Rigidity of partially hyperbolic diffeomorphisms in 3D.
method Introducing autonomous dynamical systems to prove rigidity.
result Rigidity of partially hyperbolic diffeomorphisms on 3-manifolds.
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.
Generalizes energy-momentum method for non-autonomous Hamiltonian systems.
problem Stability analysis of non-autonomous Hamiltonian systems with symmetries.
method Develops a new approach to relative equilibrium points and stability conditions for non-autonomous systems.
result Conditions ensuring stability of relative equilibrium points in non-autonomous Hamiltonian systems.
This paper explores formal verification for autonomous systems, identifying limitations and proposing improvements.
problem Ensuring safety of autonomous systems like self-driving cars and drones.
method Formal verification techniques based on formal methods, analyzing three assumptions and their limitations.
result Preliminary work to improve the strength of evidence provided by formal verification.
We enhance autonomous materials research with problem-aware models.
problem Complex decision-making in autonomous materials.
method Bayesian framework, machine learning, physics-based models, operational considerations.
result Improved models reflect problem-specific structure.
The paper explores new risk models for autonomous driving.
problem Risk management and actuarial modeling for autonomous vehicles.
method Examines technical difficulties and proposes a novel risk model.
result The new model better reflects real-world driving safety.
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.
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.
GOL uses semi-parametric approach to learn from single examples in autonomous driving.
problem Training deep neural networks for autonomous driving requires manual annotation.
method Generative One-Shot Learning (GOL) framework that learns from single examples and regularization samples.
result Generative One-Shot Learning (GOL) generates synthetic data as Pareto optimal solutions.
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.
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.
The study explores autonomous systems and their connections to contact geometry and Frobenius manifolds.
problem Understanding the connections between autonomous systems and geometric structures.
method Investigation of the Darboux-Halphen-Ramanujan system, contact geometry, and Frobenius manifolds.
result Highlighting the role of contact geometry in autonomous systems.
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.
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.
Deep RL framework teaches cars to drive autonomously.
problem Difficult to apply reinforcement learning to autonomous driving.
method Deep reinforcement learning with Recurrent Neural Networks and attention models.
result Framework successfully learned autonomous maneuvers in complex scenarios.
WiseMove framework for safe deep RL in autonomous driving.
problem Ensuring safety in deep reinforcement learning for autonomous driving.
method Modular learning architecture for motion planning.
result Demonstrated on a common traffic scenario, WiseMove supports safe learning.
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.
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.
ApolloRL offers a platform for RL research in autonomous driving.
problem Improving reinforcement learning for autonomous driving.
method Open platform with training, simulation, and evaluation components.
result Baseline agents perform well in the ApolloRL environment.
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.
Adversarial objects can fool LiDAR-based autonomous driving systems.
problem Vulnerability of LiDAR-based autonomous driving systems to adversarial attacks.
method Optimization-based approach LiDAR-Adv to generate adversarial objects.
result LiDAR-based autonomous driving systems are vulnerable to adversarial attacks.
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.
Deep RL for multi-agent autonomous driving in dynamic environments.
problem Adapting to dynamic, multi-agent driving environments.
method Formulated Partially Observable Markov Games (POSG) for multi-agent learning.
result Demonstrated successful training of multi-agent control policies.
This paper addresses challenges in designing datasets for autonomous driving.
problem Lack of robust datasets suitable for industrial deployment.
method Identifies common issues and steps to avoid them.
result Encourages better formalization of dataset design in the scientific community.
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.
Develops a method to simulate rare dangerous events in autonomous systems.
problem Rare dangerous events in safety-critical systems are hard to test in real-world settings.
method Combines exploration, exploitation, and optimization techniques for rare-event simulation.
result Provides rigorous guarantees for the performance of the method.
This paper aims to promote real-world use of DRL in autonomous driving.
problem Challenges in deploying DRL in real-world autonomous driving systems.
method Overview of AD tasks, RL algorithms, and DRL applications; discussion of deployment challenges.
result Challenges to real-world deployment of DRL in AD systems.
Simple physical modifications can fool autonomous driving systems.
problem Vulnerability of autonomous driving models to adversarial attacks.
method Demonstrated end-to-end attacks on autonomous driving using simple physical modifications.
result Simple physical modifications can induce activation patterns similar to different scenarios used in training, fooling autonomous driving models.
Gemini uses inexpensive measurements to correct biases in expensive property evaluations.
problem Accurate estimation of materials properties using expensive measurements is hindered in scientific discovery campaigns.
method Gemini is a data-driven model that corrects systematic biases between property evaluation methods using inexpensive measurements.
result Gemini reduces the number of expensive evaluations needed for Bayesian optimization in materials discovery.
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.
Safe autonomous decisions made with machine learning predictions using Conformal Decision Theory.
problem Safe decisions from imperfect machine learning predictions.
method Conformal Decision Theory framework for producing safe decisions.
result Safe decisions with provable statistical guarantees of low risk.
This work improves autonomous racing by creating diverse opponents and adapting risk.
problem Balancing performance and safety in autonomous racing environments.
method Developed a self-play method using replica-exchange Markov chain Monte Carlo for diverse opponents and a distributionally robust bandit optimization for adaptive risk adjustment.
result Demonstrated real-time motion-planning methods achieving speeds comparable to Formula One racecars.
The paper uses deep reinforcement learning to control autonomous lane changes safely.
problem Safe and efficient autonomous lane changes in vehicles.
method Deep Q-networks and quadratic approximators for decision-making and control.
result Demonstrated effectiveness in simulations for decision-making and control.
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.
The paper evaluates Bayesian neural networks for safety in autonomous driving.
problem Safety guarantees for deep neural network controllers in autonomous driving.
method Developed a framework using a state-of-the-art simulator to evaluate Bayesian controllers.
result Bayesian inference methods can provide statistical guarantees for uncertainty computation in autonomous driving.
AI mirrors modern math's autonomous development, raising interpretive challenges.
problem AI's effectiveness in math mirrors historical autonomy of math.
method Analyzes historical evolution of modern mathematics and AI's role.
result AI's affinity with math's historical autonomy suggests interpretive limits.
NAIS-Net stabilizes deep networks using non-autonomous dynamical systems.
problem Stabilizing deep neural networks to prevent vanishing/exploding gradients.
method NAIS-Net uses non-autonomous dynamical systems with skip connections to enforce stability.
result NAIS-Net proves to be globally asymptotically stable and reduces generalization gap.
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.
The paper contains a geometrization of the autonomous multi-time Lagrangian function of electrodynamics. We point out that this multi-time Lagrangian function comes from electrodynamics and the theory of bosonic strings.
DeepRacing uses neural networks to predict trajectories for autonomous racing in video games.
problem Training algorithms for high-speed autonomous racing in realistic environments.
method Developed a virtual testbed using F1 video games, trained neural networks to predict trajectories and control commands.
result Trajectory prediction outperforms end-to-end control methods in autonomous racing simulations.
This review covers methods for autonomous driving including tracking, prediction, and decision making.
problem Improving autonomous driving through better tracking, prediction, and decision making.
method Approaches based on neural networks, stochastic techniques, and reinforcement learning are discussed.
result Effective methods for autonomous driving are identified and compared.