Driver-WM is a driver-centric latent world model for causal rollout of in-cabin dynamics conditioned on out-cabin traffic, unifying kinematics forecasting with behavioral and emotional recognition via dual-stream architecture and gated injection.
End-to- end autonomous driving: Challenges and frontiers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):10164–10183, 2024a
6 Pith papers cite this work, alongside 445 external citations. Polarity classification is still indexing.
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HilDA pre-trains LiDAR backbones via multi-layer and global distillation from vision models plus temporal occupancy diffusion, yielding SOTA results on detection, flow, and occupancy tasks.
Primary-path enclosure plus τ_exp-aligned fusion on a Dual-SoC AD-ECU yields 296 ms mean shutter-to-planner latency within a 350 ms budget while co-running modular and E2E paths.
ChainFlow-VLA unifies autoregressive causal trajectory modes with VLM-conditioned diffusion refinement to reach 94.85 on NAVSIM v1, matching human performance.
UniTrans pretrains a bank of translator experts and learns combination coefficients from modality mappings in a scene-invariant latent space to enable zero-shot any-to-any feature translation for heterogeneous collaborative perception.
Industry practitioners identified 12 ADS testing challenges, prioritized two for end-to-end systems, and found that most of the 17 examined research studies lack direct applicability to real industrial contexts.
citing papers explorer
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Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
Driver-WM is a driver-centric latent world model for causal rollout of in-cabin dynamics conditioned on out-cabin traffic, unifying kinematics forecasting with behavioral and emotional recognition via dual-stream architecture and gated injection.
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HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training
HilDA pre-trains LiDAR backbones via multi-layer and global distillation from vision models plus temporal occupancy diffusion, yielding SOTA results on detection, flow, and occupancy tasks.
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An Exposure-Time-Aligned Primary-Path Architecture for Autonomous-Driving ECUs
Primary-path enclosure plus τ_exp-aligned fusion on a Dual-SoC AD-ECU yields 296 ms mean shutter-to-planner latency within a 350 ms budget while co-running modular and E2E paths.
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ChainFlow-VLA: Causal Flow Planning with Vision-Language Models
ChainFlow-VLA unifies autoregressive causal trajectory modes with VLM-conditioned diffusion refinement to reach 94.85 on NAVSIM v1, matching human performance.
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One Model to Translate Them All: Universal Any-to-Any Translation for Heterogeneous Collaborative Perception
UniTrans pretrains a bank of translator experts and learns combination coefficients from modality mappings in a scene-invariant latent space to enable zero-shot any-to-any feature translation for heterogeneous collaborative perception.
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From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry
Industry practitioners identified 12 ADS testing challenges, prioritized two for end-to-end systems, and found that most of the 17 examined research studies lack direct applicability to real industrial contexts.