Liying Liu

Jiahui Li, Jiawei Sun, Zixiang Ren et al.

Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compact scene tokens for downstream trajectory generation and scoring. While these scene tokens form a compact visual bottleneck for the planner, they receive supervision solely from the planning objective, providing limited constraints on the encoded visual information. To address this limitation, we introduce Neural Token Reconstruction (NTR), a representation learning framework to directly constrain the compact scene-token bottleneck in perception-free driving. NTR introduces a self-distillation masked latent reconstruction objective that reconstructs masked patch-level latent features using only compact scene tokens as reconstruction memory. This forces reconstruction gradients to pass exclusively through the scene-token bottleneck, encouraging scene tokens to preserve richer and less redundant visual representations for planning. We further introduce semantic priors derived from foundation-model annotations as a weak semantic interface biasing reconstruction targets toward driving-related structures without introducing explicit perception heads. All auxiliary reconstruction components are removed at inference time, leaving the deployed planner unchanged. NTR achieves state-of-the-art performance on three public autonomous driving benchmarks, including 8.0461 RFS on Waymo E2E and 94.1 PDMS / 90.9 EPDMS on NavSim1&2. The learned scene tokens exhibit lower pairwise redundancy and higher effective rank, indicating that effective bottleneck supervision improves both compact visual representation learning and planning performance.

Mingbo Li, Liying Liu, Ye Luo

Convolutional neural networks have become increasingly deep and complex, leading to higher computational costs. While tropical convolutional neural networks (TCNNs) reduce multiplications, they underperform compared to standard CNNs. To address this, we propose two new variants - compound TCNN (cTCNN) and parallel TCNN (pTCNN)-that use combinations of tropical min-plus and max-plus kernels to replace traditional convolution kernels. This reduces multiplications and balances efficiency with performance. Experiments on various datasets show that cTCNN and pTCNN match or exceed the performance of other CNN methods. Combining these with conventional CNNs in deeper architectures also improves performance. We are further exploring simplified TCNN architectures that reduce parameters and multiplications with minimal accuracy loss, aiming for efficient and effective models.