Search Results for author:
Found 21 papers, 8 papers with code
RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing.
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets.
Your Room is not Private: Gradient Inversion Attack on Reinforcement Learning
The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advancements in computer vision and large language models.
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature.
Learning to View: Decision Transformers for Active Object Detection
Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment.
Solving Coupled Differential Equation Groups Using PINO-CDE
This framework integrates engineering dynamics and deep learning technologies and may reveal a new concept for CDEs solving and uncertainty propagation.
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments.
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations.
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.
CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation
Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems.
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance
Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness.
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances.
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on different environments is still an open problem.
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance.
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.
Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method
We then train the generative model as an agent (or a generator) to investigate the risky distribution parameters for a given driving algorithm being evaluated.
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms.
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing.
MTGAN: Speaker Verification through Multitasking Triplet Generative Adversarial Networks
In this paper, we propose an enhanced triplet method that improves the encoding process of embeddings by jointly utilizing generative adversarial mechanism and multitasking optimization.
Sound Audio and Speech Processing
Vehicle Pose and Shape Estimation through Multiple Monocular Vision
We utilize state-of-the-art convolutional neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a Cross Projection Optimization (CPO) method to estimate the 3D pose.
