EDBT 2026 Demo / reviewers in the wild / expert
Linhuan Song
dblp:228/4738
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Autonomous driving · 50% Robot navigation and mapping · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Autonomous driving
vehicle control |
0.4 | 1 | 2020 | Driver-automation shared steering control for highly automated vehicles · Sci. China Inf. Sci. 2020 |
Methods — techniques the papers use, named apart from their topics
shared control · 0.4automation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Driver-automation shared steering control for highly automated vehicles
Jun Liu 0086, Hongyan Guo, Linhuan Song, Qikun Dai, Hong Chen 0003 |
Sci. China Inf. Sci. | 3 |
| 2018 | Hazard-evaluation-based Driver-automation Switched Shared Steering Control for Intelligent VehiclesabstractThe driving model switched between an intelligent vehicle and a human driver is a hot discussing issue for intelligent driving system, and it relates to the safety of the intelligent vehicle and traffic efficiency of transportation system. It presents a hazard-evaluation-based driver-automation switched shared steering control approach for intelligent vehicles in this manuscript. The switched operation between human driver and autopilot system is carried out when the hazard situation is tested by the autopilot controller. The driver's operation and the deviation from the road center line are employed to carry out the hazard evaluation. The autopilot controller is designed using the constrained model predictive control (MPC) approach to keep the intelligent vehicle run in the safe area that is between the road boundary. In order to verify the control performance of the proposed algorithm, simulation verification under hazard situation of the proposed approach are carried out and compared with the non-switching method. The results show that the intelligent vehicle can keep safe in the hazard situation. Jun Liu 0086, Linhuan Song, Hongyan Guo, Yunfeng Hu 0003, Hong Chen 0003 |
Intelligent Vehicles Symposium | 3 |