15 citations · 27 across the 15 of their papers we have counts for
8 papers · 1 filter
PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting
Tongtong Zhang, Zhiyong Cui, Bingzhang Wang +4
In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sens…
Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model
Lening Wang, Wenzhao Zheng, Dalong Du +8
4D driving simulation is essential for developing realistic autonomous driving simulators. Despite advancements in existing methods for generating driving scenes, significant chall…
Proactive security defense: cyber threat intelligence modeling for connected autonomous vehicles
Yinghui Wang, Yilong Ren, Zhiyong Cui +1
Cybersecurity has become a crucial concern in the field of connected autonomous vehicles. Cyber threat intelligence (CTI), as the collection of cyber threat information, offers an…
A dataset for cyber threat intelligence modeling of connected autonomous vehicles
Yinghui Wang, Yilong Ren, Hongmao Qin +3
Cyber attacks have become a vital threat to connected autonomous vehicles in intelligent transportation systems. Cyber threat intelligence, as the collection of cyber threat inform…
VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
Xuan Cai, Zhiyong Cui, Xuesong Bai +4
Autonomous vehicles (AVs) face significant threats to their safe operation in complex traffic environments. Adversarial training has emerged as an effective method of enabling AVs…
KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models
Kemou Jiang, Xuan Cai, Zhiyong Cui +7
Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner. These LLM-enhanced methodologies excel…
OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving
Lening Wang, Wenzhao Zheng, Yilong Ren +4
Understanding the evolution of 3D scenes is important for effective autonomous driving. While conventional methods mode scene development with the motion of individual instances, w…
Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models
Zhengxing Lan, Hongbo Li, Lingshan Liu +4
Predicting the future trajectories of dynamic traffic actors is a cornerstone task in autonomous driving. Though existing notable efforts have resulted in impressive performance im…