Demo Data from VLDB 2026 / reviewers in the wild / expert
Takamasa Horibe
dblp:182/4383
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
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 · 33% Robot navigation and mapping · 33% Motion planning and robot control · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › collision avoidance
collision-free trajectory |
0.6 | 1 | 2022 | Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach · ICRA 2022 |
Robotics › Robot navigation and mapping
obstacle avoidance |
0.6 | 1 | 2022 | Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach · ICRA 2022 |
Robotics › Autonomous driving › planning for self-driving vehicles
velocity planning |
0.6 | 1 | 2022 | Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
linear programming · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming ApproachabstractVelocity Planning for self-driving vehicles in a complex environment is one of the most challenging tasks. It must satisfy the following three requirements: safety with regards to collisions; respect of the maximum velocity limits defined by the traffic rules; comfort of the passengers. In order to achieve these goals, the jerk and dynamic objects should be considered, however, it makes the problem as complex as a non-convex optimization problem. In this paper, we propose a linear programming (LP) based velocity planning method with jerk limit and obstacle avoidance constraints for an autonomous driving system. To confirm the efficiency of the proposed method, a comparison is made with several optimization-based approaches, and we show that our method can generate a velocity profile which satisfies the aforementioned requirements more efficiently than the compared methods. In addition, we tested our algorithm on a real vehicle at a test field to validate the effectiveness of the proposed method. Yutaka Shimizu, Takamasa Horibe, Fumiya Watanabe, Shinpei Kato |
ICRA | 2 |