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.

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.

An adaptive cruise control system includes an information acquisition unit having a main detector and a secondary detector, a control unit, and an execution unit. The main detector detects an object located ahead of the vehicle. The control unit determines whether control of the vehicle is required depending on an actual value determined by the main detector and the threshold value of a system property characterizing the driving environment. The execution unit controls the vehicle. The secondary detector is arranged such that its field of view for detecting an object located at an angle ahead of the vehicle covers the boundary of the main detector's field of view, and which is oriented outwards in relation to the forward direction of the vehicle. The secondary detector sends an indication signal to adjust the threshold value of the system property when an object is detected.

An adaptive cruise control system includes an information acquisition unit having a main detector and a secondary detector, a control unit, and an execution unit. The main detector detects an object located ahead of the vehicle. The control unit determines whether control of the vehicle is required depending on an actual value determined by the main detector and the threshold value of a system property characterizing the driving environment. The execution unit controls the vehicle. The secondary detector is arranged such that its field of view for detecting an object located at an angle ahead of the vehicle covers the boundary of the main detector's field of view, and which is oriented outwards in relation to the forward direction of the vehicle. The secondary detector sends an indication signal to adjust the threshold value of the system property when an object is detected.

A system and method of detecting and mitigating an erratic vehicle by a host vehicle. The method includes gathering sensor information on a calibratable external region surrounding the host vehicle; analyzing the sensor information to detect a target vehicle traveling in a lane and a movement of the target vehicle in the lane; determining whether the movement of the target vehicle in the lane is erratic; if erratic then designating target vehicle as erratic vehicle; assigning a risk score to the erratic vehicle; and implementing a predetermined mitigating action correlating to the assigned risk score to the erratic vehicle. The mitigating action includes one or more of: warning an operator of the host vehicle, warning a vehicle proximal to the host vehicle, and taking at least partial control of the host vehicle to further distance the host vehicle apart from the erratic vehicle.

Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.

Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.

A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

A vehicle is provided that includes a cruise control deactivation system. The system includes a cruise control system, and a user control that, when activated, commands deactivation of the cruise control system. The system also includes a processor configured to permit or override the commanded deactivation of the cruise control system while the vehicle is moving, based on at least one criterion. Criteria may include whether or not a first sensor detects a foot of a driver of the vehicle on an accelerator pedal of the vehicle, and whether or not a first computation indicates that the deactivation of the cruise control system will cause a collision with a second vehicle located behind the vehicle.