most citedA Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving

7 citations · 8 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SP2022

Min-Max Latency Optimization Based on Sensed Position State Information in Internet of Vehicles

Pengzun Gao, Long Zhao, Kan Zheng +1

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the…

cs.IT2019

Twin-Timescale Radio Resource Management for Ultra-Reliable and Low-Latency Vehicular Networks

Haojun Yang, Kan Zheng, Long Zhao +1

To efficiently support safety-related vehicular applications, the ultra-reliable and low-latency communication (URLLC) concept has become an indispensable component of vehicular ne…

cs.LG20191 cited

Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks

Lingyi Han, Kan Zheng, Long Zhao +2

Short-term road traffic prediction (STTP) is one of the most important modules in Intelligent Transportation Systems (ITS). However, network-level STTP still remains challenging du…

cs.LG20197 cited

A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving

Shiwen Liu, Kan Zheng, Long Zhao +1

In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential…

eess.SP2019

Cooperative V2X for High Definition Map Transmission Based on Vehicle Mobility

Fangfei Wang, Dong Guan, Long Zhao +1

High-definition (HD) map transmission is considered as a key technology for automatic driving, which enables vehicles to obtain the precise road and surrounding environment informa…