15 citations · 27 across the 15 of their papers we have counts for
18 papers
Detectors Learn the Wrong Thing: Shortcut-Resistant Adversarial Training Against Physically Realizable Attacks
Yuanhao Huang, Yilong Ren, Jinlei Wang +3
AI-enabled visual perception systems are increasingly deployed in intelligent transportation infrastructure and autonomous vehicle related applications. However, physically realiza…
AdvSerial: Physical Adversarial Attacks on Infrastructure-mounted Pedestrian Detectors via Semantic Feature Suppression
Yuanhao Huang, Yilong Ren, Jinlei Wang +3
AI-based visual perception systems are increasingly deployed in infrastructure surveillance, including roadside monitoring units, highway cameras, and smart-city pedestrian managem…
SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration
Zhiyuan Liu, Daocheng Fu, Pinlong Cai +5
High-fidelity and controllable 3D simulation is essential for addressing the long-tail data scarcity in Autonomous Driving (AD), yet existing methods struggle to simultaneously ach…
AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction
Ruikai Li, Xinrun Li, Mengwei Xie +12
Online High-Definition (HD) map construction is pivotal for autonomous driving. While recent approaches leverage historical temporal fusion to improve performance, we identify a cr…
Stability Under Scrutiny: Benchmarking Representation Paradigms for Online HD Mapping
Hao Shan, Ruikai Li, Han Jiang +8
As one of the fundamental modules in autonomous driving, online high-definition (HD) maps have attracted significant attention due to their cost-effectiveness and real-time capabil…
MapKD: Unlocking Prior Knowledge with Cross-Modal Distillation for Efficient Online HD Map Construction
Ziyang Yan, Ruikai Li, Zhiyong Cui +7
Online HD map construction is a fundamental task in autonomous driving systems, aiming to acquire semantic information of map elements around the ego vehicle based on real-time sen…
SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Yuqi Fan, Zhiyong Cui, Zhenning Li +2
Reliable planning is crucial for achieving autonomous driving. Rule-based planners are efficient but lack generalization, while learning-based planners excel in generalization yet…
AdvReal: Physical Adversarial Patch Generation Framework for Security Evaluation of Object Detection Systems
Yuanhao Huang, Yilong Ren, Jinlei Wang +4
Autonomous vehicles are typical complex intelligent systems with artificial intelligence at their core. However, perception methods based on deep learning are extremely vulnerable…
Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test
Xuan Cai, Xuesong Bai, Zhiyong Cui +4
Autonomous driving (AD) testing constitutes a critical methodology for assessing performance benchmarks prior to product deployment. The creation of segmented scenarios within a si…
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…
AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model
Lening Wang, Yilong Ren, Han Jiang +9
Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic…