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Behavioral intention prediction in driving scenes: A survey
In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
A survey of mix-based data augmentation: Taxonomy, methods, applications, and explainability
Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …
networks. The basic idea of DA is to construct new training data to improve the model's …
T4p: Test-time training of trajectory prediction via masked autoencoder and actor-specific token memory
Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …
multiple actors and the surrounding environment. While data-driven approaches have been …
Empowering autonomous driving with large language models: A safety perspective
Learning representation for anomaly detection of vehicle trajectories
Predicting the future trajectories of surrounding vehicles based on their history trajectories is
a critical task in autonomous driving. However, when small crafted perturbations are …
a critical task in autonomous driving. However, when small crafted perturbations are …
[PDF][PDF] Exploring backdoor attacks against large language model-based decision making
Abstract Large Language Models (LLMs) have shown significant promise in decisionmaking
tasks when fine-tuned on specific applications, leveraging their inherent common sense and …
tasks when fine-tuned on specific applications, leveraging their inherent common sense and …
Safety-assured speculative planning with adaptive prediction
Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
Safety-driven interactive planning for neural network-based lane changing
Neural network-based driving planners have shown great promises in improving task
performance of autonomous driving. However, it is critical and yet very challenging to ensure …
performance of autonomous driving. However, it is critical and yet very challenging to ensure …