Search Results for author:
Found 10 papers, 2 papers with code
Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of the deep neural networks within the AD system, particularly in out-of-distribution and uncertain data.
State-Wise Safe Reinforcement Learning With Pixel Observations
In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety violations, particularly in complex environments with contact-rich or non-smooth dynamics, and when dealing with high-dimensional pixel observations.
Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling
However, it remains a challenging problem for these methods to ensure that the generated/predicted trajectories are physically realistic.
Learning Representation for Anomaly Detection of Vehicle Trajectories
We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.
Semi-supervised Semantics-guided Adversarial Training for Robust Trajectory Prediction
In this paper, we present a novel adversarial training method for trajectory prediction.
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions.
A Tool for Neural Network Global Robustness Certification and Training
Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction
In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks.
TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor
Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making.
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.
