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.

Opportunities to purchase products from online shopping sites continue to increase. One feature of online shopping sites is searching for products by entering queries on input forms. Analyzing user search behavior is critical for appropriate search support and sales improvement. In this paper, we assigned five search behavior codes from continuous logs: replacing keywords (R), modifying keywords (M), adding keywords (A), deleting keywords (D), and continuing same keywords (C). Using code sequences, we analyzed frequent search patterns and the relationship between initial search patterns and search length. As a result of evaluation experiments, we found that behavior, which moves to another search result page without changing the query, occupies a majority of search behavior. Search behavior also becomes longer when it follows the initial stage of the search.