19 citations · 33 across the 6 of their papers we have counts for
7 papers
Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinement
Jianbo Zhao, Taiyu Ban, Xiangjie Li +5
The inherent sequential modeling capabilities of autoregressive models make them a formidable baseline for end-to-end planning in autonomous driving. Nevertheless, their performanc…
Autoregressive Meta-Actions for Unified Controllable Trajectory Generation
Jianbo Zhao, Taiyu Ban, Xiyang Wang +6
Controllable trajectory generation guided by high-level semantic decisions, termed meta-actions, is crucial for autonomous driving systems. A significant limitation of existing fra…
DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling
Jianbo Zhao, Taiyu Ban, Zhihao Liu +7
Accurate and efficient modeling of agent interactions is essential for trajectory generation, the core of autonomous driving systems. Existing methods, scene-centric, agent-centric…
KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation
Jianbo Zhao, Jiaheng Zhuang, Qibin Zhou +7
Trajectory generation is a pivotal task in autonomous driving. Recent studies have introduced the autoregressive paradigm, leveraging the state transition model to approximate futu…
Causal Structure Learning Supervised by Large Language Model
Taiyu Ban, Lyuzhou Chen, Derui Lyu +2
Causal discovery from observational data is pivotal for deciphering complex relationships. Causal Structure Learning (CSL), which focuses on deriving causal Directed Acyclic Graphs…
Integrating Large Language Model for Improved Causal Discovery
Taiyu Ban, Lyuzhou Chen, Derui Lyu +4
Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causa…
Mitigating Prior Errors in Causal Structure Learning: A Resilient Approach via Bayesian Networks
Lyuzhou Chen, Taiyu Ban, Xiangyu Wang +2
Causal structure learning (CSL), a prominent technique for encoding cause-and-effect relationships among variables, through Bayesian Networks (BNs). Although recovering causal stru…