7 accepted papers
Online High-Definition (HD) map construction is pivotal for autonomous driving. While recent approaches leverage historical temporal fusion to improve performance, we identify a critical safety flaw in this paradigm: it is inherently "spatially backward-looking." These methods predominantly enhance
As one of the fundamental intermediate modules in autonomous driving, online high-definition (HD) maps have attracted significant attention due to their cost-effectiveness and real-time capabilities. Since vehicles always cruise in highly dynamic environments, spatial displacement of onboard sensor…
Simulating driving environments in 4D is crucial for developing accurate and immersive autonomous driving systems. Despite progress in generating driving scenes, challenges in transforming views and modeling the dynamics of space and time remain. To tackle these issues, we propose a fresh methodolog…
Reliable planning is crucial for achieving autonomous driving. Rule-based planners are efficient but lack generalization, while learning-based planners excel in generalization yet have limitations in real-time performance and interpretability. In long-tail scenarios, these challenges make planning p…
Pedestrian trajectory prediction has emerged as a core component of human-robot interaction and autonomous driving. Fast and accurate prediction of surrounding pedestrians contributes to making decisions and improves safety and efficiency. However, pedestrians’ future trajectories will interact with…
Semi-supervised learning (SSL) is a machine learning paradigm that utilizes both labeled and unlabeled data to enhance the performance of learning tasks. However, SSL methods operate under the assumption that the label spaces of labeled and unlabeled data are identical, which may not hold in open-wo…
The success of deep learning is largely attributed to the training over clean data. However, data is often coupled with noisy labels in practice. Learning with noisy labels is challenging because the performance of the deep neural networks (DNN) drastically degenerates, due to confirmation bias caus…