A driving support control device for a vehicle includes an acquisition unit configured to acquire, from a first detector which detects change of a brightness value of an object which occurs in accordance with displacement of the object, information indicating change of a brightness value of a partially shielded object partially shielded by an obstacle which occurs in accordance with displacement of the partially shielded object, as a first detection signal, and a control unit configured to, in a case where it is determined, by using the first detection signal, that the partially shielded object is moving, cause a driving support execution device to execute collision prevention support for preventing a collision with the partially shielded object.

A navigation system for a host vehicle may include at least one processor programmed to receive images representative of an environment of the host vehicle. The processor may analyze the images to identify navigational state information associated with the host vehicle; determine a plurality of potential trajectories for the host vehicle based on the navigational state information; perform a preliminary analysis to select a subset of the plurality of potential trajectories; perform a secondary analysis to select one of the subset of the plurality of potential trajectories as a planned trajectory for the host vehicle; determine one or more navigational actions for the host vehicle based on the planned trajectory; and cause at least one adjustment of a navigational actuator of the host vehicle to implement the one or more navigational actions for the host vehicle.

An intelligent harvester with instantaneous stop functions includes a harvester body, a detecting system for detecting a human body within an area range on a traveling route of the harvester body, and a brake system arranged on the harvester body and operatively connected to the detecting system. When the detecting system detects a human body present within the area range on a traveling path of the harvester body, the brake system controls the harvester body to brake.

Methods and systems for enhancing the functionality of a semi-autonomous vehicle are described herein. The semi-autonomous vehicle may receive a communication from a fully autonomous vehicle within a threshold distance of the semi-autonomous vehicle. If the vehicles are travelling on the same route or the same portion of a route, the semi-autonomous vehicle may navigate to a location behind the fully autonomous vehicle. Then the semi-autonomous vehicle may operate autonomously by replicating one or more functions performed by the fully autonomous vehicle. The functions and/or maneuvers performed by the fully autonomous vehicle may be detected via sensors in the semi-autonomous vehicle and/or may be identified by communicating with the fully autonomous vehicle to receive indications of upcoming maneuvers. In this manner, the semi-autonomous vehicle may act as a fully autonomous vehicle.

Provided is a vehicle control device which, when traffic participants are waiting in the vicinity of a railroad crossing for a train to pass, appropriately controls driving of a vehicle about to pass through the railroad crossing. Entry of the vehicle into the railroad crossing is restrained until a waiting time elapses since when the railroad crossing transitioned from a passage blocking state to a passage allowing state, the waiting time being set in accordance with the kind or the number of the traffic participants present in the vicinity of the railroad crossing. When the waiting time has elapsed, the vehicle is caused to enter the railroad crossing and pass (through) the railroad crossing.

Provided are methods for light-based object localization, which can include comparing unexpected light sources to expected light sources for determination of an agent, such as a partially and/or fully occluded agent. Some methods described also include generating a trajectory for an autonomous vehicle based on the comparison. Systems and computer program products are also provided.

Methods for operating a vehicle in an environment include receiving light detection and ranging (LiDAR) data from a LiDAR of the vehicle. The LiDAR data represents objects located in the environment. A dynamic occupancy grid (DOG) is generated based on a semantic map. The DOG includes multiple grid cells. Each grid cell represents a portion of the environment. For each grid cell, a probability density function is generated based on the LiDAR data. The probability density function represents a probability that the portion of the environment represented by the grid cell is occupied by an object. A time-to-collision (TTC) of the vehicle and the object less than a threshold time is determined based on the probability density function. Responsive to determining that the TTC is less than the threshold time, a control circuit of the vehicle operates the vehicle to avoid a collision of the vehicle and the object.

An autonomous tractor for autonomously crossing farmland, includes one or more sensors for detection of an obstacle when crossing the farmland, and a central processing unit (CPU) for receiving input signals from the the sensors and for controlling movement of the tractor based on the input signals in order to avoid the obstacle, a coupler for coupling an agricultural machine able to be coupled to the tractor, and the agricultural machine includes one or more additional sensors for detection of the obstacle, wherein coupling of the agricultural machine operatively connects the additional sensors to the CPU and automatically provides data to the CPU regarding the location of each of the additional sensors on the agricultural machine and one or more specifications of each of these additional sensors.

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

A method, apparatus, and system for estimating a moving speed of a detected pedestrian at an autonomous driving vehicle (ADV) is disclosed. A pedestrian is detected in a plurality of frames of point clouds generated by a LIDAR device installed at an autonomous driving vehicle (ADV). In each of at least two of the plurality of frames of point clouds, a minimum bounding box enclosing points corresponding to the pedestrian excluding points corresponding to limbs of the pedestrian is generated. A moving speed of the pedestrian is estimated based at least in part on the minimum bounding boxes across the at least two of the plurality of frames of point clouds. A trajectory for the ADV is planned based at least on the moving speed of the pedestrian. Thereafter, control signals are generated to drive the ADV based on the planned trajectory.