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Found 6 papers, 1 papers with code
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.
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.
Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework
While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating.
Cryptography and Security
