Toyota Jidosha Kabushiki Kaisha

Hideyuki Matsui

Taichi Kawanai

Kazuhiko Kamikado

Nobuhide Kamata

Yusuke Hayashi

Masahiro Harada

Eiji Sakaguchi

Keisuke Hokai

Hideo Fukamachi

Yasuhiro Takagi

Junya Watanabe

Toshiki Kindo

Norihiro Takahashi

Junya Ueno

Hiroshi Sakamoto

Ryosuke Fukatani

Tomoyuki Kuriyama

Yasuhiro Kobatake

Sadayuki Abe

Hayato Ito

Kazuki Tamura

An autonomous driving device includes a first map including a first content and a second content associated with positions respectively, a second map including the first content associated with the position and in which the second content is not a recording target, and a control unit performing the autonomous driving. When the autonomous driving is performed using the first map, the control unit determines information necessary for the autonomous driving using a first method based on the first content and the second content. When the autonomous driving is performed using the second map, the control unit determines the information necessary for the autonomous driving using a method same as the first method based on the first content corresponding to a second position recorded in the second map and information indicating that the second content corresponding to the second position recorded in the second map is not present.

Autonomous driving device

A self-position estimation accuracy verification method includes a step of moving a mobile object to a first check point, a step of acquiring first check information, a step of starting a self-position estimation using the first check information as an initial value, a step of moving the mobile object to a second check point while continuing the self-position estimation, a step of acquiring second check information, and a step of calculating a deviation between a position and a posture of the mobile object on the map estimated by the self-position estimation at the second check point and a position and a posture of the mobile object on the map indicated in the second check information, and verifying the accuracy of the self-position estimation based on the deviation.

Self-position estimation accuracy verification method and self-position estimation system

A map information system includes a map database including map information; and a driving assist level determination device. The map information is associated with an evaluation value indicating a certainty of the map information for each location in an absolute coordinate system. Information indicating that the intervention operation is performed is included in driving environment information indicating a driving environment of a vehicle. The driving assist level determination device is configured to acquire, based on the driving environment information, intervention operation information indicating an intervention operation location where the intervention operation is performed, acquire, based on the map information, the evaluation value for each point or section in a target range, and determine, based on the evaluation value and the intervention operation location, an allowable level for each point or section within the target range.

Map information system

An autonomous driving system that performs a traveling of an autonomous driving vehicle based on a remote instruction by a remote commander includes: a vehicle position acquisition unit configured to acquire a position of the autonomous driving vehicle on the map; an external environment recognition unit configured to recognize an external environment of the autonomous driving vehicle; a remote instruction location situation recognition unit configured to recognize a remote instruction location situation on a target route of the autonomous driving vehicle set in advance based on the target route, the position of the autonomous driving vehicle on the map, and map information; and a remote instruction request determination unit configured to determine whether or not to request the remote commander for the remote instruction with regard to the remote instruction location situation, based on the external environment of the autonomous driving vehicle.

Autonomous driving system

An autonomous driving device includes a map recording a content having different type for each position while one or a plurality of contents and positions are associated with each other, an acquisition unit acquiring the content corresponding to a first position on the map, a specification storage unit storing a plurality of autonomous driving modes of the vehicle and the type of content necessary for the execution of the modes in association with each other, a selection unit selecting an executable autonomous driving mode based on the type of content acquired by the acquisition unit and the type of content stored in the specification storage unit, and a control unit controlling the vehicle at the first position in the selected autonomous driving mode, the selection unit determines one autonomous driving mode based on an order of priority set in advance when there is a plurality of executable autonomous driving modes.

In step S, a color image CIMG is acquired. In step S, a distance image DIMG is acquired. In step S, the color image CIMG is projected onto the distance image DIMG. An alignment of the color image CIMG and the distance image DIMG is performed prior to the projection of the color image CIMG. In step S, it is determined whether or not a basic condition is satisfied. In S, it is determined whether or not a special condition is satisfied. If a judgement result of the steps Sor Sis positive, then in step Sa first data point and a second data point on the distance image DIMG are associated. If bot of the judgement results of steps Sand Sare negative, data points are not associated in step S

Object recognition device

An object recognition apparatus include a storage device and a processor. The storage device stores peripheral information and tolerance information. The tolerance information is information in which the degree of tolerance for the undetected object is represented for each class of the object. The peripheral information is acquired by a sensor device provided in the vehicle. The processor performs object recognition process for recognizing an object around the vehicle. In the object recognition process, the processor identifies the object and its class to be detected based on the peripheral information, and calculates the likelihood that is a parameter representing the probability of detection of the object. Further, the processor calculates a likelihood threshold corresponding to the object based on the tolerance information, and determines whether to output the identification result of the object based on the comparative between the likelihood and the likelihood threshold.

Object recognition apparatus

A vehicle control system includes a vehicle control apparatus configured to set a target trajectory of a vehicle in autonomous driving, a manual driving database configured to contain manual driving trajectory information that indicates a manual driving trajectory that is a trajectory of the vehicle in manual driving, a weight acquisition device configured to acquire weight information that indicates weights of the target trajectory and manual driving trajectory, the weights being designated by a user of the vehicle, and a trajectory adjusting device configured to determine an integrated target trajectory by integrating the target trajectory and the manual driving trajectory based on the weights indicated by the weight information. The vehicle control apparatus is configured to control the autonomous driving of the vehicle such that the vehicle follows the integrated target trajectory.

Vehicle control system and vehicle control method

A map information update system includes a map database; a server; a vehicle position recognition unit configured to recognizes a vehicle position of a vehicle on a map; an external environment information generation unit configured to generate external environment information related to an external environment of the vehicle in association with the vehicle position on the map based on the vehicle position on the map and a detection result of a sensor mounted on the vehicle; a map freshness calculation unit configured to calculate map freshness per area set in advance on map information; a awarding unit configured to award a user of the vehicle or the vehicle an incentive point which is set in advance in accordance with the map freshness of the area corresponding to the external environment information, when the external environment information is transmitted to the server from the vehicle; and a user interaction unit.

Growing community of inventors

Shizuoka-ken, Japan

Hideyuki Matsui