According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.

A method for controlling a coasting guide function is provided. The method may include: detecting a speed limit and an average speed of a peripheral vehicle; detecting a valid speed limit when a coasting event occurs; and calculating a target speed by using a speed factor computed by using at least one of the valid speed limit, the average speed of the peripheral vehicle, or a current speed.

A method for enriching a map representation of a traffic infrastructure. The method includes: receiving map data of a map representation of a traffic infrastructure, the map representation including pieces of information of a multitude of roadways negotiable by vehicles, the map representation encompassing pieces of information regarding at least one roadway including a transition area; receiving trajectory data of a multitude of first driving trajectories and a multitude of second driving trajectories of a multitude of vehicles; ascertaining a first number of the first driving trajectories and a second number of the second driving trajectories, and comparing the first number to the second number; and marking the first route as a preferred route in the map representation if the first number is greater than the second number.

A vehicle includes a driving device configured to control a speed of the vehicle, a camera configured to detect a surrounding vehicle, and a controller configured to determine the speed of the vehicle. The controller also calculates an image vector variation amount of the surrounding vehicle when the speed of the vehicle is lower than a predetermined speed and calculates a safety distance between the vehicle and a preceding vehicle based on the image vector variation amount of the surrounding vehicle when the image vector variation amount of the surrounding vehicle satisfies a predetermined condition. The controller also controls the driving device to control the speed of the vehicle depending on the calculated safety distance.

A system and method for adaptive cruise control with proximate vehicle detection are disclosed. The example embodiment can be configured for: receiving input object data from a subsystem of a host vehicle, the input object data including distance data and velocity data relative to detected target vehicles; detecting the presence of any target vehicles within a sensitive zone in front of the host vehicle, to the left of the host vehicle, and to the right of the host vehicle; determining a relative speed and a separation distance between each of the detected target vehicles relative to the host vehicle; and generating a velocity command to adjust a speed of the host vehicle based on the relative speeds and separation distances between the host vehicle and the detected target vehicles to maintain a safe separation between the host vehicle and the target vehicles.

A method of operating an autonomous vehicle includes detecting, based on an input received from a sensor of an autonomous vehicle that is being navigated by an on-board computer system, an occurrence of a driving event, making a determination by the on-board computer system, upon the detecting the occurrence of the driving event, whether or how to alter the path planned by the on-board computer system according to a set of rules, and performing further navigation of the autonomous vehicle based on the determination until the driving event is resolved. The driving event may include a presence of an object in a shoulder area of the road. The driving event may include accumulation of more than a certain number of vehicles behind the autonomous vehicle. The driving event may include a slow vehicle ahead of the autonomous vehicle. The driving event may include a do-not-change-lane zone is within a threshold.

Trajectory value learning for autonomous systems includes generating an environment image from sensor input and processing the environment image through an image neural network to obtain a feature map. Trajectory value learning further includes sampling possible trajectories to obtain a candidate trajectory for an autonomous system, extracting, from the feature map, feature vectors corresponding to the candidate trajectory, combining the feature vectors into the input vector, and processing, by a score neural network model, the input vector to obtain a projected score for the candidate trajectory. Trajectory value learning further includes selecting, from the candidate trajectories, the candidate trajectory as a selected trajectory based on the projected score, and implementing the selected trajectory.

While operating a host vehicle in a first lane on a road, respective first statistical probabilities of a) an average speed of a plurality of vehicles, including the host vehicle, operating in the first lane, b) an average distance between the vehicles operating in the first lane, and c) an average number of braking events performed by the vehicles operating in the first lane are determined based on sensor data. A high traffic density probability for the road is determined based on the first statistical probabilities. A host vehicle assist feature is transitioned between an enabled state and a disabled state based on the high traffic density probability for the road being greater than a threshold probability.

A method for assembling a vehicle from a set of modules for travelling a planned route, wherein the set of modules comprises at least one functional module and a plurality of drive modules. Each drive module comprises a pair of wheels, electrical motor, and an interface releasably connectable to a corresponding interface on another module, wherein each drive module is configured to operate autonomously and has an individual set of energy parameters. The method comprising obtaining route information associated with route segments of the planned route, selecting a first drive module having an individual set of energy parameters matching route information associated with a first route segment and selecting a second drive module having an individual set of energy parameters matching route information associated with a second route segment, and thereafter commanding the drive modules to connect together and with a functional module.

A vehicular vision system includes a front camera disposed at a windshield of a vehicle equipped with the system and a side camera disposed at a side mirror of the vehicle. An electronic control unit (ECU) includes an image processor for processing image data captured by the cameras. The vehicular vision system, responsive to processing by the image processor of image data captured by the front camera, detects a leading vehicle traveling in front of the equipped vehicle in a traffic lane the equipped vehicle is also traveling along. The vehicular vision system, responsive to determination of an intent of the equipped vehicle to change lanes and to processing by the image processor of image data captured by the side camera, detects an oncoming vehicle traveling in a traffic lane adjacent to the traffic lane the equipped vehicle is traveling along.