Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

The systems and methods described herein are related to terrain-based insights for advanced driver assistance systems (ADAS) in vehicles. Such terrain-based insights may be related to ADAS features such as adaptive cruise control, lane keep assist, automatic emergency braking, collision avoidance, and/or speed adaptation, among others.

Techniques are provided for autonomous vehicle fleet management for reduced traffic congestion. A request is received for a vehicular ride. The request includes an initial spatiotemporal location and a destination spatiotemporal location. A processor is used to generate a representation of lane segments. Each lane segment is weighted in accordance with a number of other vehicles on the lane segment. A vehicle located within a threshold distance to the initial spatiotemporal location is identified such that the identified vehicle has at least one vacant seat. The processor is used to determine a route for operating the identified vehicle from the initial spatiotemporal location to the destination spatiotemporal location. The route includes one or more lane segments of the lane segments. An aggregate of weights of the one or more lane segments is below a threshold value. The received request and the determined route are transmitted to the identified vehicle.

Terrain characteristics of an off-road area are determined based on a map. The terrain characteristics include a terrain type, a terrain grade, and a presence or an absence of an obstacle. Vehicle characteristics are determined including a ground clearance and a breakover angle. Based on a user level, vehicle parameters for the off-road area are determined based on the terrain characteristics, the vehicle characteristics, and the user input. The vehicle parameters include a speed and a transmission gear. The vehicle parameters for the off-road area are output.

A method of controlling a vehicle when the vehicle passes over a speed bump, may include: dividing sections of the road into a first section within a first time period before the front wheel of the vehicle collides with the speed bump, a second section while the front wheel collides with the speed bump, a third section within a second time period before the rear wheel collides with the speed bump, and a fourth section while the rear wheel collides with the speed bump; and controlling and distributing at least one of suspension damping force, driving power and braking force to the front wheel and the rear wheel for each of the first section, the second section, the third section and the fourth section to reduce the amount of impact to be applied when the vehicle collides with the speed bump and to reduce a vertical motion of the vehicle that occurs while the vehicle goes over the speed bump.

An advanced driver assistance system for a heavy-duty vehicle. The ADAS includes a road geometry determining device arranged to determine a geometry of a road section in a forward direction ahead of the vehicle, and a vehicle motion management module configured to predict a swept area by the vehicle when driving in the forward direction, based on a geometric model of the vehicle and on a current vehicle control command, wherein the swept area by the vehicle comprises an area traversed by an overhang of the vehicle. The ADAS further includes a display device configured to illustrate the geometry of the road section and the predicted swept area by the vehicle in dependence of the current vehicle control command.

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle. The at least one processor of the system may be further programmed to determine a navigational path for the vehicle through the free space region and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle.

A driving assistance method for controlling an own vehicle by a controller in such a way that the own vehicle travels along a target travel trajectory includes: acquiring level-difference information of a level difference existing along a lane in which the own vehicle travels; and when, from the level-difference information, determining that the own vehicle is to climb over the level difference, generating the target travel trajectory in such a way that a level-difference climbing-over angle, which is an angle formed by the level difference and the target travel trajectory, is larger than a threshold value.

According to various aspects, a vehicle may include: one or more image sensors configured to provide sensor image data representing a sensor image of a vicinity of the vehicle; one or more processors configured to determine one or more obstacles from the sensor image data, the one or more obstacles corresponding to one or more image objects of the sensor image, determine a distance from ground for each of the one or more obstacles based its corresponding image object, and trigger a safety operation when the distance from ground is equal to or less than a safety height associated with the vehicle.