Researchers at Beihang University, China, introduce a new task setting: latency-aware trajectory prediction for autonomous driving, which explicitly accounts for the latency issue and transforms it from a hindrance into an opportunity for enhanced performance.
The team published their study in Communications in Transportation Research ( https://doi.org/10.26599/COMMTR.2026.9640010 ).
"We propose a novel latency-aware trajectory prediction framework empowered by a consolidated auxiliary learning paradigm, which opens a fundamentally different pathway toward practical trajectory forecasting", says Haiyang Yu, a professor at the School of Transportation Science and Engineering at Beihang University.
In this study, the research group decouples prediction into two tasks: a primary task that predicts valid-horizon trajectories from historical data, and an auxiliary task that utilizes latency-inclusive observations. By allowing the auxiliary branch access to latency-crafted inputs, LatenAux is then committed to transferring latency-aware knowledge to the primary branch via a progressive feature alignment strategy. This enables the primary model to internalize latency cues without explicit reliance on latency data. LatenAux departs from conventional auxiliary learning by introducing a soft feature-consistency function, which gradually incorporates auxiliary representations across both scene context and query state levels, enriching features while avoiding overconstraint. In addition, auxiliary queries act as informative priors for the primary branch to further enhance prediction accuracy.
"We conduct extensive experiments on two large-scale real-world datasets to validate the proposed model. The results demonstrate the effectiveness and superiority of LatenAux, showing that it consistently supports latency-aware modeling and delivers more accurate and reliable trajectory forecasts." Zhengxing Lan, a Ph.D. candidate, says.
"The demonstrated adaptability of LatenAux across different latency durations effectively transforms a fundamental system constraint into a feature, ensuring that downstream planning modules receive more reliable forecasts. This inherent flexibility allows LatenAux to remain highly effective across autonomous driving systems with diverse hardware capabilities." Lingshan Liu explains.
About Communications in Transportation Research
Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Xiaopeng (Shaw) Li from University of Wisconsin–Madison. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.
It has been indexed in SCIE, SSCI, Ei Compendex, Scopus, CSTPCD, CSCD, OAJ, DOAJ, TRID and other databases. It was selected as Q1 Top Journal in the Engineering and Technology category of the Chinese Academy of Sciences (CAS) Journal Ranking List. In 2022, it was selected as a High-Starting-Point new journal project of the "China Science and Technology Journal Excellence Action Plan". In 2024, it was selected as the Support the Development Project of "High-Level International Scientific and Technological Journals". The same year, it was also chosen as an English Journal Tier Project of the "China Science and Technology Journal Excellence Action Plan PhaseⅡ". In 2024, it received the first impact factor (2023 IF) of 12.5, ranking Top1 (1/58, Q1) among all journals in "TRANSPORTATION" category. In 2025, its 2024 IF was announced as 14.5, maintaining the Top1 position (1/62, Q1) in the same category.
From Volume 6 (2026), Communications in Transportation Research will be published by Tsinghua University Press on the SciOpen platform with the official journal website at https://www.sciopen.com/journal/2097-5023 . We kindly request that all new manuscript submissions be made through the journal's submission system at https://mc03.manuscriptcentral.com/commtr
