Direct C–H functionalization of tetrahydro-γ-carbolines at the α-position

Zhang, Yahui; Chen, Yi; Guo, Minyi; Sun, Guoqiang; Que, Rongbin; Cui, Ranran; Gao, Yu; Chen, Haijun

doi:10.1039/d2nj00928e

Cited by 2 publications

(2 citation statements)

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“…In traffic scene understanding and trajectory prediction or planning, various learning methods have been employed to effectively capture diverse types of inputs and model interactions among different entities. A key paradigm involves the use of agent‐centric models that provide superior accuracy over scene‐centric alternatives [5, 7, 12, 27–31]. Scene‐centric encoding schemes focus on encoding an entire scene, including all agents and their interactions, into a single representation.…”

Section: Related Workmentioning

confidence: 99%

“…Several existing studies focused on improving road graph representations using graph convolution techniques [12, 32–34]. Notably, LaneGCN [12] and BANet [27] incorporate more road graph elements and interaction fusion blocks to enhance the modelling of complex interactions among diverse inputs. Other models, such as SceneTransformer [28] and Wayformer [5] employ multi‐axis attention for spatial and temporal information modelling, separating the processes of learning agent‐to‐agent and agent‐to‐road interactions.…”

Section: Related Workmentioning

confidence: 99%

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Multi‐agent trajectory prediction with adaptive perception‐guided transformers

Nguyen,

Yoo

2024

IET Intelligent Trans Sys

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The ability to predict the trajectory of an autonomous vehicle accurately is crucial for safe and efficient navigation. However, predicting diverse and multimodal futures can be challenging. Recent approaches such as attention and graph neural networks have achieved state‐of‐the‐art performance by considering agent interactions and map contexts. This study focused on multi‐agent prediction using an agent‐centric approach with transformers. This enables parallel computation and a comprehensive understanding of the environment. Two main features are introduced: an adaptive receptive field (ARF) that captures the relevant surroundings for each agent, and perception encoding, which serves as spatial context embeddings. The ARF adapts to the agent's velocity and rotation, focusing attention ahead at high speeds or to the sides when it is slower. Perception encoding divides agents or lanes into levels and encodes the information of each level. This approach enables the efficient encoding of complex spatial relationships. The proposed method combines these advances with transformer modelling for multi‐agent trajectory prediction while ensuring real‐time prediction capabilities. The approach is evaluated on the Argoverse benchmark and better performance than the state‐of‐the‐art baseline is achieved. By addressing challenges such as multimodal outputs and robustness, the study enhances the safety and efficiency of autonomous driving systems by more accurately predicting trajectories.

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Section: Related Workmentioning

confidence: 99%