Various technologies described herein pertain to controlling an autonomous vehicle to provide indicators that signal a driving intent of the autonomous vehicle. The autonomous vehicle includes a plurality of sensor systems that generate a plurality of sensor signals, a notification system, and a computing system. The computing system determines that the autonomous vehicle is to execute a maneuver that will cause the autonomous vehicle to traverse a portion of a driving environment of the autonomous vehicle. The computing system predicts that a person in the driving environment is to traverse the portion of the driving environment based upon the plurality of sensor signals. The computing system then controls the notification system to output a first indicator indicating that the autonomous vehicle plans to yield to the person or a second indicator indicating that the autonomous vehicle plans to execute the maneuver prior to the person traversing the portion of the driving environment.

A method for supporting autonomous driving of an autonomous vehicle includes detecting, by an in-vehicle internet-of-things (IoT) platform of the autonomous vehicle, a vulnerable road user (VRU) having a mobile device in a vicinity of the autonomous vehicle. A mobility application runs on the mobile device of the VRU and sends VRU-specific data to the in-vehicle IoT platform of the autonomous vehicle. The VRU is detected based on the VRU-specific data and/or in-vehicle sensor data of the autonomous vehicle. The method further includes determining, by the in-vehicle IoT platform, a movement intention prediction based on the VRU-specific data. The movement intention prediction is computed by use of a machine learning model. The VRU-specific data of the mobile device are provided as input data for the machine learning model. In addition, the method includes performing an autonomous driving decision for the autonomous vehicle based on the movement intention prediction.

A method for handling a situation relating to a vehicle and/or a third party includes ascertaining or predicting a critical situation relating to the vehicle and/or the third party; detecting an object in the surroundings of the vehicle and/or the third party; and controlling the vehicle and/or the third party relative to the object or controlling the object relative to the vehicle and/or the third party such that in a first phase, a comparably rapid or accelerated approach is carried out between the vehicle and/or the third party and the object; in a second phase, a comparably slow or decelerated approach is carried out between the vehicle and/or the third party and the object; and in a third phase, a contact is produced between the vehicle and/or the third party and the object.

A vehicle control device includes a recognizer configured to recognize a surrounding environment of a vehicle, a setter configured to set a first risk area in a surrounding area of the vehicle on the basis of a recognition result of the recognizer, and a controller configured to control at least one of a speed and steering of the vehicle. The setter sets the first risk area so that the first risk area includes an area between the moving object and a first end of a crosswalk where the moving object is scheduled to arrive in the crosswalk when the moving object is entering the crosswalk which is provided in front of the vehicle and where the vehicle is scheduled to pass on the basis of the recognition result of the recognizer. The controller prevents the vehicle from entering the first risk area when a first predetermined condition is satisfied.

Among other things, techniques are described for expressive vehicle systems. These techniques may include obtaining, with at least one processor, data associated with an environment, the environment comprising a vehicle and at least one object; determining an expressive maneuver including a deceleration of the vehicle such that the vehicle stops at least a first distance away from the at least one object and the vehicle reaches a peak deceleration when the vehicle is a second distance away from the at least one object; generating data associated with control of the vehicle based on the deceleration associated with the expressive maneuver; and transmitting the data associated with the control of the vehicle to cause the vehicle to decelerate based on the deceleration associated with the expressive maneuver.

A method helps to protect an occupant of a vehicle (10) equipped with an automated driving system (200) and a vehicle safety system (100) by detecting low impact crash events (99) with the vehicle (10). The method includes utilizing automated driving sensors (220, 230, 240, 250, 260) of the automated driving system (200) to identify possible low impact collision risks. The method also includes utilizing vehicle safety system sensors (110, 115, 120, 125, 130) of the vehicle safety system to determine a low impact collision resulting from the identified possible low impact collision. A vehicle safety system (100) includes an airbag controller unit (150) configured to implement the method to determine low impact crash events with the vehicle (10).

Systems and methods for managing environmental conditions for an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes a perception sensor configured to generate perception data indicative of a condition of the environment, a network communication transceiver configured to communicate with an oversight system and an external weather condition source, a non-transitory computer readable medium, and a processor. The processor is configured to: receive the perception data from the at least one perception sensor, receive an indication of current weather conditions from the external weather condition source, determine a current environmental condition severity level from a plurality of severity levels based on the perception data and the indication of current weather conditions, modify one or more driving parameters that that govern a range of actions that can be autonomously executed by the autonomous vehicle, and navigate the autonomous vehicle based on the modified driving parameters.

According to one aspect, an overall airbag system of an autonomous vehicle uses a combination of sensors to deploy one or more external airbags in advance of a collision or as a collision occurs, for the purpose of protecting vulnerable persons. By using sensor data, combined with perception and prediction algorithms, an autonomous driving system may deploy a substantially optimal combination of external airbags for a given the size of a vulnerable person, vehicle speed, and/or collision timing. In addition to cushioning impact during a collision, the deployment of multiple external airbags may also control kinematics of vulnerable persons, as for example by addressing brain injuries in pedestrians due to rotational kinematics.

A system may receive point cloud data that includes one or more data points associated with an object that was detected by sensors of an autonomous vehicle. The system may identify a subset of the point cloud data having data points that are associated with a likelihood of a pedestrian entering a scene with the object, determine a current probability value using a logistic function that is associated with the subset of the point cloud data, determine, based at least in part on the current probability value, a probability value representing a likelihood of the pedestrian actually being present for the subset of the point cloud data, determine whether the probability value exceeds a false alarm threshold value, and in response to the probability value exceeding the false alarm threshold value, assign data points of the subset an attribute value indicative of the pedestrian being present.

Methods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Embodiments may further include determining, based on the determined ACMs, whether one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement, and initiating a cooperative driving engagement with the one or more identified vehicles in response to determining that the one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement.