5 papers
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…
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…
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…
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…