A method of path planning for a host vehicle includes: receiving host vehicle, environmental and obstacle information; calculating one or more projected host vehicle locations; computing a projected obstacle location for each obstacle; and determining a collision potential between each projected host vehicle location and each projected obstacle location. Until a maximum number of steps is reached, and while at least one projected host vehicle location has an associated collision potential below a collision threshold, the method further includes repeating the calculating, computing and determining steps.

A vehicle control apparatus includes an interaction determination unit, an occupant emotion acquisition unit, and a vehicle controller. The interaction determination unit is configured to determine a second vehicle that interacts with a first vehicle during automated driving. The occupant emotion acquisition unit is configured to acquire an emotion of an occupant of the first vehicle. The vehicle controller is configured to perform vehicle control of the first vehicle, on the basis of the emotion of the occupant of the first vehicle and an emotion of an occupant of the second vehicle that has been determined to interact with the first vehicle by the interaction determination unit.

A driving support apparatus according to the invention estimates the position of a moving body by controlling a position estimation unit when the tracking-target moving body leaves a first area or a second area to enter a blind spot area and detects the position of the moving body by controlling a position detection unit when the moving body leaves the blind spot area to enter the first area or the second area. In this manner, the trajectory of the tracking-target moving body is calculated so that the trajectory of the moving body detected in the first area or the second area and the trajectory of the moving body estimated in the blind spot area are continuous to each other and driving support is executed based on the calculated trajectory of the tracking-target moving body.

Example embodiments relate to techniques for three dimensional (3D) object detection and localization. A computing system may cause a radar unit to transmit radar signals and receive radar reflections relative to an environment of a vehicle. Based on the radar reflections, the computing system may determine a heading and a range for a nearby object. The computing system may also receive an image depicting a portion of the environment that includes the object from a vehicle camera and remove peripheral areas of the image to generate an image patch that focuses upon the object based on the heading and the range for the object. The image patch and the heading and the range for the object can be provided as inputs into a neural network that provides output parameters corresponding to the object, which can be used to control the vehicle.

Techniques for safe non-conservative planning include: obtaining a risk budget constraining a plan for an autonomous vehicle to satisfy an objective; based at least on the risk budget and the objective, planning a trajectory of the autonomous vehicle toward the objective, at least by: (a) determining a risk cost associated with an initial planned action of the trajectory, (b) based at least on the risk cost, determining whether the trajectory is feasible or infeasible within the risk budget, and (c) responsive to determining that the trajectory is feasible within the risk budget, executing the initial planned action; decreasing the risk budget by the risk cost, to obtain a remaining risk budget; obtaining state data corresponding to a state of the autonomous vehicle after executing the initial planned action; and based at least on the state data, the remaining risk budget, and the objective, planning another trajectory toward the objective.

A method for determining a dynamic vehicle distance between a following vehicle and a preceding vehicle of a platoon, wherein a V2V signal is configured to be transmitted in a wireless manner between the following vehicle and the preceding vehicle, includes determining a current maximum following vehicle deceleration of the following vehicle, determining a current transmission time for transmitting information from the preceding vehicle to the following vehicle, determining a current maximum preceding vehicle deceleration of the preceding vehicle, and determining the dynamic vehicle distance comprising a transmission distance and a braking distance difference. The transmission distance indicates a distance traveled by the following vehicle between the preceding vehicle initiating an emergency braking procedure and the following vehicle initiating an emergency braking procedure. The transmission distance is dependent upon the current transmission time.

A method and an apparatus for planning a speed of an autonomous vehicle, and a storage medium are provided. The method includes: generating a plurality of speed trajectories to be selected for the autonomous vehicle before the autonomous vehicle changes a lane; obtaining predicted speeds and predicted positions of an obstacle at the plurality of time points; calculating a cost function value of each of the speed trajectories to be selected, according to the plurality of speed trajectories to be selected, the predicted speeds and the predicted positions; and selecting a speed trajectory to be selected with a minimum cost function value as a planning speed trajectory of the autonomous vehicle. A speed of the autonomous vehicle can be adjusted before the autonomous vehicle changes a lane, thereby creating an opportunity and a safe distance for the lane change, and improving a success rate of the lane change.

In some examples, a network computer system can monitor a plurality of mobile computing devices to determine a current location of a corresponding freight operator of a plurality of freight operators. The network computer system can record the current location of each of the plurality of freight operators in a data store of the set of memory resources. Additionally, the network computer system can repeatedly query the data store to determine when at least two freight operators of the plurality of freight operators that satisfy a set of drafting conditions. The set of drafting conditions including a proximity condition as between the at least two freight operators and a candidate commencement location. In response to the determination, the network computer system can implement a drafting arrangement between the at least two freight operators.

The subject disclosure relates to features that reduce computational overhead needed to transport sensor data through an autonomous vehicle (AV) network. In some aspects, a process of the disclosed technology includes steps for receiving a plurality of sensor packets at an accelerated AV network pipeline, extracting frame information associated with the plurality of sensor packets, and extracting payload data from the plurality of sensor packets based on the associated frame information to produce a plurality of de-capsulated sensor packets. Systems and machine-readable media are also provided.

Systems and methods for driving trajectory planning of an automated vehicle. The system includes an electronic processor configured to determine a lane segment graph indicating allowable transitions between a plurality of lane segments. The electronic processor is also configured to determine a current type of traffic flow situation. The electronic processor is further configured to determine weighting factors for each of the allowable transitions based on aggregate observations of previous real-world traffic flow transitions for the current type of traffic flow situation. Each of the weighting factors indicate a likelihood of transition for a respective one of the allowable transitions. The electronic processor is also configured to determine a weighted lane segment graph based at least in part on the weighting factors. The electronic processor is further configured to determine a driving trajectory of the automated vehicle based at least in part on the weighted lane segment graph.