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Takamasa Horibe

dblp:182/4383 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › collision avoidance
collision-free trajectory
0.612022
Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach · ICRA 2022
Robotics › Robot navigation and mapping
obstacle avoidance
0.612022
Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach · ICRA 2022
Robotics › Autonomous driving › planning for self-driving vehicles
velocity planning
0.612022
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
YearPublicationVenuePosition
2022 Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach
abstract
Velocity 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
ICRA2