Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

An autonomous vehicle includes an array of sensors, a processor, and a switch. The array of sensors generate sensor data related to one or more objects in an external environment of the autonomous vehicle and the processor determines an environmental context. The switch transfers the sensor data from the array of sensors to the processor, where the switch is configured to: (a) receive first sensor data from a first sensor group of the array of sensors; (b) receive second sensor data from a second sensor group of the array of sensors; (c) determine an order of transmission of the first sensor data over the second sensor data in response to the environmental context; and (d) transmit the first sensor data to the processor prior to transmitting the second sensor data based on the order of transmission.

Systems, methods, and computer program products to enhance the situational competency and/or the safe operation of a vehicle, when operating at least partially in an autonomous mode, as a support vehicle for one or more on-road persons engaged in a training or competitive cycling, running, and/or walking activity on a predetermined travel route at a predetermined pace.

Methods and systems for controlling navigation of a vehicle are disclosed. The system will first detect a URU within a threshold distance of a drivable area that a vehicle is traversing or will traverse. The system will then receive perception information relating to the URU, and use a plurality of features associated with each of a plurality of entry points on a drivable area boundary that the URU can use to enter the drivable area to determine a likelihood that the URU will enter the drivable area from that entry point. The system will then generate a trajectory of the URU using the plurality of entry points and the corresponding likelihoods, and control navigation of the vehicle while traversing the drivable area to avoid collision with the URU.

Sensor data is received from an array of sensors configured to capture one or more objects in an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on proximity data or environmental contexts. First sensor data from the first sensor group is prioritized for transmission based on the proximity data or environmental contexts.

A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

As an example, data identifying characteristics of a road user as well as contextual information about the vehicle's environment is received from the vehicle's perception system. A prediction of the intent of the object including an action of a predetermined list of actions to be initiated by the road user and a point in time for initiation of the action is generated using the data. A prediction of the behavior of the road user for a predetermined period of time into the future indicating that the road user is not going to initiate the action during the predetermined period of time is generated using the data. When the prediction of the behavior indicates that the road user is not going to initiate the action during the predetermined period of time, the vehicle is maneuvered according to the prediction of the intent prior to the vehicle passing the object.

A driving assistance device recognizes traveling environment information on a vehicle, detects a moving object having a speed component from an outside of a traveling road of the vehicle to an inside of the traveling road, determines whether the moving object will collide with the vehicle based on movement information of the vehicle and the moving object, performs execution of emergency braking if a physical quantity indicating a correlation between the vehicle and the moving object is equal to or smaller than a preset threshold, and cancels the execution of the emergency braking if the physical quantity is equal to or smaller than the threshold and a structure that blocks entry of the moving object into the traveling road is present on a movement path of the moving object.

The disclosure relates collecting feedback from passengers of autonomous vehicles. For instance, that a triggering circumstance for triggering a feedback request has been met may be determined. The triggering circumstance may include a driving event, a presence of other road users, or a trip state. A display requirement and data collection parameters for the feedback request are identified based on the determination. The display requirement defines when the feedback request is displayed and the data collection parameters identify information that the feedback request is to collect. The feedback request is provided for display based on the display requirement and data collection parameters. In response, feedback from a passenger of the autonomous vehicle is received and stored for later use.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include inputting data into a model and receiving an output from the model representing a discretized representation. The discretized representation may be associated with a probability of an object reaching a location in the environment at a future time. A vehicle computing system may determine a trajectory and a weight associated with the trajectory using the discretized representation and the probability. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory and the weight output by the vehicle computing system.