An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example system for an AV includes obtaining, by a computer located in the AV, an image from a camera located on the AV, where the image characterizes an area towards which the AV is driven on a lane on a road or a highway; determining, from the image, that a pedestrian or a cyclist is located next to the lane on the road or the highway; and in response to the determining, performing driving operations on the AV such as steering from a center of the lane to a first side of the lane that is away from the center of the lane and away from a location of the pedestrian or the cyclist, and/or slowing down the AV in response to certain conditions.

A braking control system includes obstacle detection means for detecting an obstacle ahead of a vehicle, first collision determination means for determining whether the vehicle would collide with the obstacle ahead of the vehicle, following vehicle detection means for detecting a following vehicle traveling behind the vehicle, information acquisition means for acquiring a maximum deceleration set in the following vehicle, second collision determination means for determining whether the following vehicle would collide with the vehicle based on the maximum deceleration, and braking control means for controlling braking means of the vehicle so that an absolute value of a deceleration of the vehicle does not exceed an absolute value of the maximum deceleration of the following vehicle when the first collision determination means determines that the vehicle would collide with the obstacle and the second collision determination means determines that the following vehicle would collide with the vehicle.

The disclosed technology provides solutions for generating simulated scenes to facilitate autonomous vehicle (AV) testing. In some implementations, the disclosed technology encompasses methods for generating simulated scenes that can includes steps for receiving road data, wherein the road data comprises sensor data collected for a recorded scene measured using one or more vehicle-mounted sensors, processing the road data to generate semantic scene data, and generating a simulated scene based on the semantic scene data. Systems and machine-readable media are also provided.

A vehicle control method is provided such that when a host vehicle is stopped at a front of a vehicle line of vehicles in accordance with a stop signal of a traffic light at an intersection, the engine is stopped by using an idle stop control. When either a left-turn or a right-turn will be made after the traffic light changes to a go signal, a presence or an absence of a traveling body, which is stopped on a side of or behind the host vehicle in a direction from which the host vehicle is turning, is detected during the stop signal. Upon determining the traveling body is stopped on the side of host vehicle, restarting of the engine is placed on standby even when the traffic light turns to the go signal, and the engine is restarted in accordance with a behavior of the traveling body.

The present invention relates to a method for avoiding collisions of a moving vehicle with other road users in the surroundings of the vehicle, comprising at least the method steps of: a) detecting, by means of one or more sensors, the vehicle surroundings and the other road users located therein; b) dividing the vehicle surroundings into a plurality of occupied areas; c) classifying the other road users detected in method step a), wherein, by means of the classification, at least one road user group is assigned to each of the other road users; d) prioritising the road user classified in method step c), taking into account both the classification carried out in method step c) and the occupied area defined in method step b), wherein road users from one or more predetermined road user groups in the particular occupied area are given a high priority and road users from other, non-predetermined road user groups in the particular occupied area are given a lower priority; and e) determining the probability of collision of the other road users with the vehicle, wherein the collision probability is determined in accordance with the prioritisation carried out in method step d) and the collision probability of the other road users having a high priority is determined first; f) changing or maintaining the current driving behaviour of the vehicle on the basis of the collision probabilities determined in method step e).

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to receive geoindexed detected vehicle event data from at least one vehicle occurring within a predefined time period, normalize the geoindexed detected vehicle event data according to Global Position System (GPS) data corresponding to a geoindex map, and generate an infrastructure recommendation based on the normalized and aggregated geoindexed detected vehicle event data.

An autonomous vehicle computing system can include a primary perception system configured to receive a plurality of sensor data points as input generate primary perception data representing a plurality of classifiable objects and a plurality of paths representing tracked motion of the plurality of classifiable objects. The autonomous vehicle computing system can include a secondary perception system configured to receive the plurality of sensor data points as input, cluster a subset of the plurality of sensor data points of the sensor data to generate one or more sensor data point clusters representing one or more unclassifiable objects that are not classifiable by the primary perception system, and generate secondary path data representing tracked motion of the one or more unclassifiable objects. The autonomous vehicle computing system can generate fused perception data based on the primary perception data and the one or more unclassifiable objects.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. Example embodiments relate to an autonomous vehicle having a trailer coupled to a rear thereof. An example method includes continuously predicting a trailer trajectory that is distinct from a planned trajectory of the autonomous vehicle. The method further includes determining that the predicted trailer trajectory is within a minimum avoidance distance away from a stationary vehicle located on a roadway on which the autonomous vehicle is located. The method further includes modifying the planned trajectory of the autonomous vehicle such that the predicted trailer trajectory satisfies the minimum avoidance distance. The method further includes causing the autonomous vehicle to navigate along the modified trajectory based on transmitting instructions to one or more subsystems of the autonomous vehicle.

Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.