Disclosed embodiments include techniques for providing alerts to a driver of a vehicle. The techniques include detecting a condition that exceeds a threshold hazard potential; detecting a predetermined gesture of a driver, the predetermined gesture indicating that the driver has acknowledged the detected condition; in response to detecting the predetermined gesture, reducing an urgency level for alerting the driver of the detected condition; and determining whether to issue an alert to the driver based on the reduced urgency level.

Disclosed are an apparatus and a method for controlling driving of a vehicle. The apparatus includes a sensor that obtains information about an external environment of the vehicle, and a controller that calculates a target driving speed based on at least one of weather information, road surface information, or a minimum sensing distance detectable by the sensor, and sets a preset driving speed as the target driving speed. Accordingly, the driving of the vehicle is controlled by accurately reflecting external environment information and setting the target driving speed of the vehicle, so that it is possible to minimize user anxiety about the stability of autonomous driving and reduce the risk of an accident.

An apparatus comprising at least one interface to receive sensor data from a plurality of sensors of a vehicle; and one or more processors to autonomously control driving of the vehicle according to a path plan based on the sensor data; determine that autonomous control of the vehicle should cease; send a handoff request to a remote computing system for the remote computing system to control driving of the vehicle remotely; receive driving instruction data from the remote computing system; and control driving of the vehicle based on instructions included in the driving instruction data.

A method for processing autonomous driving simulation data. The method includes: determining a type of a message transmitted between a simulation system and an auto driving system (ADS); determining a data acquisition mode based on the type of the message; obtaining a data stream transmitted between the simulation system and the ADS based on the data acquisition mode; and determining performance of the ADS based on the data stream.

A method for ascertaining features in an environment of at least one mobile unit for implementation of a localization and/or mapping by a control unit. In the course of the method, sensor measurement data of the environment are received, the sensor measurement data received are transformed by an alignment algorithm into a cost function and a cost map is generated with the aid of the cost function, a convergence map is generated based on the alignment algorithm. At least one feature is extracted from the cost map and/or the convergence map and stored, the at least one feature being provided in order to optimize a localization and/or mapping. A control unit, a computer program, and a machine-readable storage medium are also described.

An automated driving system (ADS) of an autonomous vehicle includes a communication module, a misbehavior detection module, and a processor. The communication module is configured to receive a vehicle-to-vehicle (V2V) message including source vehicle data and receive a fusion data message including fusion data from a mobile edge computing (MEC) system including a roadside unit (RSU). The source vehicle data includes a source vehicle location. The fusion data is based on RSU sensed data and on vehicle sensed data received at the RSU from at least one vehicle. The misbehavior detection module is configured to determine whether a source vehicle is disposed at the source vehicle location based on the fusion data. The processor is configured to manage performance of the autonomous vehicle in accordance with the source vehicle data based at least in part on the determination. Other embodiments are described and claimed.

The present disclosure relates to a system and a method for addressing an error in a localization system that includes monitoring a plurality of sensors of a driver assistance system in real-time, with each sensor generating a data stream. The method further includes identifying a sensor having an anomalous data stream and calculating a primary localization and a backup localization. The primary localization calculation includes the anomalous data stream and the backup localization calculation does not include the anomalous data stream. Further, the method includes executing an action when the backup localization error estimate exceeds a threshold.

An annotation handling system for in an on edge-based manner training a supervised or semi-supervised perception model on edge of a vehicle equipped with an ADS. The annotation handling system stores while the vehicle is being driven, sensor data; selects annotation-eligible data out of the sensor data; generates a learning model candidate by annotating an event in the annotation-eligible data using a perception learning model; generates at least a first corroboration candidate by annotating the event based on perception predictions of the event derived from radar- and/or lidar-based sensor data of the obtained sensor data and/or based on identifying the event in a digital map; determines when one or more of the at least first corroboration candidate match the learning model candidate fulfilling corroboration criteria, an annotation of the event based on the learning model candidate and the first corroboration candidate; and updates the perception model based on the annotation.

A vehicle control device (1) includes an automated driving control unit (100) configured to execute automated driving of a vehicle and an acquisition unit (124) configured to acquire movement states in a running direction and a lateral direction of the vehicle. The automated driving control unit is configured to execute the automated driving such that the movement states which have been acquired by the acquisition unit before the automated driving has been started are maintained in a predetermined time or a predetermined distance when automated driving has been started by switching from manual driving.

The reliability of path planning calculation can be evaluated with low load without using multiplexing of calculation or an additional sensor. The electronic control device includes an integration unit that acquires information around a vehicle as sensor information from a plurality of sensors for each processing cycle, and integrates the acquired sensor information to create vehicle peripheral information for each processing cycle, a path planning unit that calculates a planned path on which the vehicle will travel in the future using the vehicle peripheral information for each processing cycle, and a path evaluation unit that evaluates reliability of the path planning unit, in which the path evaluation unit that uses, in the planned path calculated by the path planning unit in a first processing cycle, a position of the vehicle in a second processing cycle, which is a processing cycle after the first processing cycle, and the vehicle peripheral information created by the integration unit in the second processing cycle to evaluate the reliability of the path planning unit in the first processing cycle.