A method includes receiving sensor data associated with one or more inputs associated with a road portion, determining a level of risk associated with each of the one or more inputs, determining an estimated amount of computing resources that each of a plurality of candidate computing methods will consume, and selecting one or more computing methods from the plurality of candidate computing methods to associate with the one or more inputs based on the levels of risk associated with the one or more inputs and the estimated amount of computing resources that the candidate computing methods will consume.

The present invention relates to a method for use in a driver assistance system of an ego-vehicle. The method supports driving of the ego-vehicle and comprises the steps of retrieving a priority relationship between the ego-vehicle and at least one traffic participant involved in a traffic situation; selecting a prediction model for the at least one traffic participant depending on the priority relationship; predicting at last one hypothetical future trajectory for the ego-vehicle and, based on the selected prediction model, at last one hypothetical future trajectory for the at least one traffic participant; and calculating a behavior relevant score for ego-vehicle based on the calculated hypothetical future trajectories.

The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

The present disclosure relates to a navigation system for a host vehicle. The system may include a processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify at least one pedestrian in the environment of the host vehicle; cause at least one adjustment of a navigational system of the host vehicle to signal to the pedestrian a navigational intent of the host vehicle; analyze the plurality of images to detect a potential reaction of the pedestrian to the at least one adjustment of the navigational system of the host vehicle; determine a navigational action for the host vehicle based on a detected potential reaction of the pedestrian; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined navigational action for the host vehicle.

If an external environment recognition unit recognizes a particular section adjacent to a first travel path, a trajectory generation unit generates a first travel trajectory that causes the own vehicle to enter the first travel path after a travel along the first travel path inside the particular section, and if the external environment recognition unit does not recognize the particular section, the trajectory generation unit generates a second travel trajectory that causes the own vehicle to directly enter the first travel path from outside the first travel path.

A method and apparatus for controlling a first vehicle autonomously are disclosed. For instance, one or more processors may plan to maneuver the first vehicle to complete an action and predict that a second vehicle will take a particular responsive action. The first vehicle is then maneuvered towards completing the action in a way that would allow the first vehicle to cancel completing the action without causing a collision between the first vehicle and the second vehicle, and in order to indicate to the second vehicle or a driver of the second vehicle that the first vehicle is attempting to complete the action. Thereafter, when the first vehicle is determined to be able to take the action, the action is completed by controlling the first vehicle autonomously according to whether the second vehicle begins to take the particular responsive action.

An environmental safety system may include first sensors each located at a predetermined physical location of a physical location and with a predetermined orientation. The system may receive first sensor data captured by the plurality of first sensors. The system may also determine values of parameters of an object within a threshold distance of the physical location using the first sensor data. The values of the parameters of the object may be transmitted to a vehicle approaching the physical location. The vehicle may receive second sensor data captured by second sensors in the vehicle. An optimized navigation of the vehicle approaching the physical location may be determined based on the values of the parameters of the object and the second sensor data. A driving action may be provided to the vehicle based on the optimized navigation of the vehicle.

Disclosed are distributed computing systems and methods for controlling multiple autonomous control modules and subsystems in an autonomous vehicle. In some aspects of the disclosed technology, a computing architecture for an autonomous vehicle includes distributing the complexity of autonomous vehicle operation, thereby avoiding the use of a single high-performance computing system and enabling off-the-shelf components to be use more readily and reducing system failure rates.

A vehicle-behavior prediction device includes: a priority determination section that determines the priority of a host vehicle and a vehicle concerned when the host vehicle and the vehicle concerned pass through a road section; and a vehicle control section that sets the time from when the vehicle concerned stops to when the host vehicle starts action to avoid the vehicle concerned to be shorter in a case where the priority of the host vehicle is low than in a case where the priority of the host vehicle is high.

A rule violation leading to a traffic conflict involving a vehicle and at least one agent in real-time driving scenarios, simulation, re-simulation or other application is determined to be the fault of the vehicle by determining whether the situation was “reasonably foreseeable,” determining whether the at least one agent is a vulnerable road user (VRU) and determining whether the at least one agent violated a higher priority rule than the rule violated by the vehicle. In an embodiment, on-vehicle decisions are based on a rulebook with a priority structure that accounts for responsibility and determines an “initiator” of the traffic conflict based on whether the vehicle or the at least one agent was the first to violate a rule and the first to violate a higher priority rule.