A method of operating an autonomous vehicle includes determining, by the autonomous vehicle, whether a target is in an intended maneuver zone around the autonomous vehicle; generating, by the autonomous vehicle, a signal in response to determining that the target is within the intended maneuver zone around the autonomous vehicle; determining, by the autonomous vehicle and based on perception information acquired by the autonomous vehicle, whether the target has left the intended maneuver zone around the autonomous vehicle; and determining, by the autonomous vehicle, that it is safe to perform the intended maneuver in response to determining, by the autonomous vehicle, that the target is not in the intended maneuver zone or in response to determining, by the autonomous vehicle, that the target has left the intended maneuver zone.

A method of operating an autonomous vehicle includes determining, by the autonomous vehicle, whether a target is in an intended maneuver zone around the autonomous vehicle; generating, by the autonomous vehicle, a signal in response to determining that the target is within the intended maneuver zone around the autonomous vehicle; determining, by the autonomous vehicle and based on perception information acquired by the autonomous vehicle, whether the target has left the intended maneuver zone around the autonomous vehicle; and determining, by the autonomous vehicle, that it is safe to perform the intended maneuver in response to determining, by the autonomous vehicle, that the target is not in the intended maneuver zone or in response to determining, by the autonomous vehicle, that the target has left the intended maneuver zone.

This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

A vehicle collects data of a plurality of to-be-detected barrier gate crossbars around the vehicle by using a sensor mounted on the vehicle, and transmits the data of the plurality of to-be-detected barrier gate crossbars to a processor; the processor determines data of a target barrier gate crossbar from the data of the plurality of to-be-detected barrier gate crossbars based on a pose of the target barrier gate crossbar, where the target barrier gate crossbar is a barrier gate crossbar of a lane on which the vehicle is located; and the processor determines a status of the target barrier gate crossbar based on the data of the target barrier gate crossbar, and controls, based on the status of the target barrier gate crossbar, the autonomous driving vehicle to pass through the target barrier gate crossbar.

A vehicle sets a first determination region of a first obstacle and a second determination region, on the basis of a position of the first obstacle. The second determination region is located at a position farther than the first determination region. A reliability of the second obstacle is set to a first reliability in a case where a position of a second obstacle falls outside the first determination region and falls outside the second determination region. The reliability of the second obstacle is set to a second reliability higher than the first reliability in a case where the position of the second obstacle falls within the first determination region or falls within the second determination region. Braking is applied to the vehicle body and/or acceleration of the vehicle body is suppressed, on the basis of the reliability of the second obstacle and the position of the second obstacle.

One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

A system for associating perceived and mapped lane edges can include a processor and a memory. The memory includes instructions such that the processor is configured to receive a sensor data representing a perceived object; receive map data representing a map object; determine a cost matrix a cost matrix indicative of an association cost for associating the map object to the perceived object; compare the association cost with an association cost threshold; and associate the perceived object with the map object based on the association cost.

A system for associating perceived and mapped lane edges can include a processor and a memory. The memory includes instructions such that the processor is configured to receive a sensor data representing a perceived object; receive map data representing a map object; determine a cost matrix a cost matrix indicative of an association cost for associating the map object to the perceived object; compare the association cost with an association cost threshold; and associate the perceived object with the map object based on the association cost.

A position and attitude estimation apparatus includes sub-sensor input accepters, a speed sensor state determiner, a scale estimator, and a position and attitude information corrector. The sub-sensor input accepter accepts an output of a sub-sensor which acquires information regarding a movement amount based on information other than an output value of a speed sensor. The speed sensor state determiner determines whether the output value of the speed sensor is reliable. The scale estimator estimates a size of the movement amount based on at least one of the output value of the speed sensor and an output value of the sub-sensor. The position and attitude information corrector corrects position and attitude information based on the size of the movement amount estimated by the scale estimator.