A fully tested autonomous system works predictably under ideal or assumed environment. However, behavior of the system is not fully defined when component(s) malfunction or fail (e.g., sensor failures) and the like which leads to inefficient task execution. Present disclosure provides system and method for imperfect sensing-based analysis of agents deployed in environments for traversal task execution, specifically, in unknown environments. The system estimates performance metric (e.g., task execution time) and safety metrics (e.g., number of collisions encountered while executing the task) related to task of traversal of the vehicle/agent from its current position to a target location. The system also incorporates sensitivity of each sensor(s), for given task, when they malfunction. The sensing malfunction can be both independent and/or co-related with malfunction of other sensors. Such a model helps to identify the most critical component in the agent(s), thereby increasing reliability of the system and meet safety standards.

A sensor system for a vehicle, including a first sensor that detects at least one collision-relevant physical variable and outputs at least one corresponding first sensor signal, and at least one second sensor that, independently of the first sensor, detects at least one collision-relevant physical variable and outputs at least one corresponding second sensor signal, and an evaluation and control unit that receives the at least one first sensor signal and the at least one second sensor signal and evaluates them for the collision detection. A method is also described for monitoring a sensor. The evaluation and control unit uses at least one second comparison signal that is based on the at least one second sensor signal of the at least one second sensor to monitor the first sensor.

The present invention obtains a rider-assistance system capable of appropriately assisting with driving by a rider of a straddle-type vehicle and a control method for such a rider-assistance system.

The rider-assistance system that assists with driving by the rider of the straddle-type vehicle includes: a peripheral environment detector that is mounted to the straddle-type vehicle and detects peripheral environment of the straddle-type vehicle; an input device that is mounted to the straddle-type vehicle and is operated by the rider of the straddle-type vehicle; and a controller that governs operation of the rider-assistance system. The controller includes: an acquisition section that acquires pitch angle correction target information that is target information on pitch angle correction of the peripheral environment detector; and a correction operation performing section that performs correction operation for detection of the peripheral environment by the peripheral environment detector on the basis of the pitch angle correction target information acquired by the acquisition section.

A vehicle control system controls a vehicle including a stop switch. A recognition sensor is configured to recognize a situation around the vehicle. The vehicle control system executes vehicle traveling control for generating a target trajectory of the vehicle based on a recognition result by the recognition sensor and executing control such that the vehicle follows the target trajectory. The vehicle control system executes the evacuation control that is the vehicle traveling control for evacuating the vehicle to the target position in a case where the vehicle traveling control is normal when the stop switch is pressed. The vehicle control system executes deceleration-and-stop control for decelerating the vehicle to stop the vehicle without using the target trajectory in a case where the vehicle traveling control is abnormal when the stop switch is pressed.

An electronic control device for a vehicle according to the present invention includes an action prediction unit that predicts the action of an object around the vehicle on the basis of external information acquired from external information detection units that detect external information of the vehicle, and a determination unit for a detection unit that determines whether an abnormality has occurred in the external information detection unit by comparing external information acquired from the external information detection unit at the time corresponding to a prediction result of the action prediction unit to the prediction result of the action prediction unit.

Embodiments of the present disclosure relate to the technical field of autonomous driving, and in particular to methods for updating an autonomous driving system, autonomous driving systems, and on-board apparatuses. In the embodiments of the present disclosure, the autonomous driving system, even when in a manual driving mode, also senses the surrounding environment of a vehicle, performs vehicle positioning, and generates an autonomous control instruction for an underlying vehicle execution system according to environment sensing information, positioning information, and data of vehicle sensors. However, the autonomous driving system does not issue an instruction to control the driving of the vehicle. Instead, it compares the instruction with a control instruction from the driver of the vehicle for the underlying vehicle execution system in the manual driving mode to update a planning and control algorithm of the autonomous driving system. As such, the updated autonomous driving system better caters to the driving habits of the driver and improves the driving experience for the driver without compromising the reliability of planning and decision-making of autonomous driving.

A device for controlling a vehicle may include a sensor that senses an object around the vehicle and acquires travel information of the vehicle, and a controller that creates an integrated line corresponding to a road where the vehicle is traveling, and sets a region of interest corresponding to the object based on the integrated line, and changes the region of interest based on the travel information of the vehicle.

A system comprises a computer including a processor and a memory, and the memory including instructions such that the processor is programmed to calculate a standard deviation of a plurality of predictions, wherein each prediction of the plurality of predictions is generated by a different deep neural network using sensor data; and determine at least one of a measurement corresponding to an object based on the standard deviation.

Systems, methods, and apparatus related to cruise control for a vehicle. In one approach, speed for a first vehicle is controlled in a first mode using data from sensors. The speed is controlled while keeping at least a minimum distance from a second vehicle being followed by the first vehicle. In response to determining that data from the sensors is not usable to control the first vehicle (e.g., the data cannot be used to measure the minimum distance), the first vehicle changes from the first mode to a second mode. In the second mode, the first vehicle maintains a constant speed and/or obtains additional data from sensors and/or computing devices located externally to the first vehicle. In another approach, the additional data can additionally or alternatively be obtained from a mobile device of a passenger of the first vehicle. The additional data is used to maintain a safe minimum distance from the second vehicle.

An approach is provided for requesting a map update based on an accident and/or damaged/malfunctioning sensors to allow a vehicle to continue driving. The approach involves determining, by one or more processors, a status of one or more sensors, one or more systems, or a combination thereof of a vehicle. The approach also involves transmitting, by the one or more processors, a request for a map update based on the status of the one or more sensors, the one or more systems, or a combination thereof of the vehicle. The approach further involves receiving, by the one or more processors, the map update in response to the request. The approach further involves configuring, by the one or more processors, at least one system of the vehicle to operate using the map update.