Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, have recently been recognized as potential alternatives to softmax attention thanks to their linear complexity and competitive performance. However, although their linear-memory advantage during training enables dealing with long sequences, it is still hard to handle extremely long sequences with very limited computational resources. In this paper, we propose Sequence Accumulation (SA) which leverages the common recu…

Abstract Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering …

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Autonomous driving systems require the ability to fully understand and predict the surrounding environment to make informed decisions in complex scenarios. Recent advancements in learning-based systems have highlighted the importance of integrating prediction and planning modules. However, this integration has brought forth three major challenges: inherent trade-offs by sole prediction, consistency between prediction patterns, and social coherence in prediction and planning. To address these challenges, we introdu…

Predicting the multimodal future motions of neighboring agents is essential for an autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an agent is affected by the complex interaction among itself, other agents, and the local roads. Unlike most existing works, which predict a fixed number of possible future motions of an agent, we propose a map-adaptive predictor that can predict a variable number of future trajectories of an agent according to the number of lanes with candidate cen…