Various technologies described herein pertain to controlling an autonomous vehicle to provide indicators that signal a driving intent of the autonomous vehicle. The autonomous vehicle includes a plurality of sensor systems that generate a plurality of sensor signals, a notification system, and a computing system. The computing system determines that the autonomous vehicle is to execute a maneuver that will cause the autonomous vehicle to traverse a portion of a driving environment of the autonomous vehicle. The computing system predicts that a person in the driving environment is to traverse the portion of the driving environment based upon the plurality of sensor signals. The computing system then controls the notification system to output a first indicator indicating that the autonomous vehicle plans to yield to the person or a second indicator indicating that the autonomous vehicle plans to execute the maneuver prior to the person traversing the portion of the driving environment.

A working machine includes a braking assembly and a throttle assembly. A controller is operatively connected to the braking assembly and the throttle assembly. A proximity sensor is operatively connected to the controller and is adapted to emit radiation away from the rear of the working machine, and to receive reflected radiation indicating the presence of a person or object within a danger zone adjacent to the rear of the working machine and within a warning zone that extends beyond the danger zone. The proximity sensor is adapted to send a signal to the controller when it detects a person or object in the danger zone to cause the braking assembly to brake the working machine, and to send a signal to the controller when it detects a person or object in the warning zone to cause the throttle assembly to reduce the speed of the working machine.

The disclosure provides an apparatus, method and system for assisting driving of a vehicle including an image sensor disposed in the vehicle to have a field of view of the front of the vehicle, configured to capture image data, a radar disposed in the vehicle to have a detecting area the outside of the vehicle, configured to capture detecting data to detect an object around the vehicle, and a controller including at least one processor configured to process the image data captured by the image sensor and the detecting data captured by the radar. The controller may obtain state information of vehicle traffic lights based on processing of the image data, in response to a right turn operation is detected at an intersection, and may control the vehicle to turn right after pausing or decelerating at a predetermined speed based on the state information of the vehicle traffic lights, information about another vehicle of a left lane detected based on processing of the detecting data, and state information of crosswalk traffic lights received from an external device. According to the disclosure it is possible to perform a right turn more safely at the intersection.

Techniques are discussed for determining predicted trajectories based on a top-down representation of an environment. Sensors of a first vehicle can capture sensor data of an environment, which may include agent(s) separate from the first vehicle, such as a second vehicle or a pedestrian. A multi-channel image representing a top-down view of the agent(s) and the environment and comprising semantic information can be generated based on the sensor data. Semantic information may include a bounding box and velocity information associated with the agent, map data, and other semantic information. Multiple images can be generated representing the environment over time. The image(s) can be input into a prediction system configured to output a heat map comprising prediction probabilities associated with possible locations of the agent in the future. A predicted trajectory can be generated based on the prediction probabilities and output to control an operation of the first vehicle.

A system and a method for autonomous decisioning and operation by an autonomous agent includes: collecting decisioning data including: collecting a first stream of data includes observation data obtained by onboard sensors of the autonomous agent, wherein each of the onboard sensors is physically arranged on the autonomous agent; collecting a second stream of data includes observation data obtained by offboard infrastructure devices, the offboard infrastructure devices being arranged geographically remote from and in an operating environment of the autonomous agent; implementing a decisioning data buffer that includes the first stream of data from the onboard sensors and the second stream of data from the offboard sensors; generating current state data; generating/estimating intent data for each of one or more agents within the operating environment of the autonomous agent; identifying a plurality of candidate behavioral policies; and selecting and executing at least one of the plurality of candidate behavioral policies.

A vehicle drive assistance system is provided, which includes a control unit configured to perform a drive assistance control based on a balance state between a driver's required driving ability required for driving a vehicle based on a traffic environment around the vehicle and drive assistance which is provided to the driver by the vehicle, and a driver's current driving ability. The control unit includes a processor configured to execute a balance determining module to determine the balance state between the required driving ability and the current driving ability based on a physical quantity related to a driving operation by the driver.

A passive infra-red guidance system and method for augmenting operation of an autonomous vehicle on a roadway includes at least one forward-looking infra-red imaging sensor mounted on the vehicle in operative communication with an image processor tied into the vehicle's operational system. The system determines the left and right edges of the roadway using thermal imaging, and then determines the centerline of the travel lane in which the vehicle is travelling based on the determined left and right edges of the roadway. The system then compares the determined centerline of the travel lane with the actual position of the vehicle and identifies any adjustment needed for the vehicle's position based on the comparison. The left and right edge determination may comprise identifying a difference between a thermal signature representative of the roadway and a thermal signature representative of a non-roadway portion that is located proximate to the roadway portion.

Aspects of the present disclosure describe systems, methods, and devices for automated vehicular control based on glare detected by an optical system of a vehicle. In some aspects, automated control includes controlling the operation of the vehicle itself, a vehicle subsystem, or a vehicle component based on a level of glare detected. According to some examples, controlling the operation of a vehicle includes instructing an automatically or manually operated vehicle to traverse a selected route based on levels of glare detected or expected along potentials routes to a destination. According to other examples, controlling operation of a vehicle subsystem or a vehicle component includes triggering automated responses by the subsystem or the component based on a level of glare detected or expected. In some additional aspects, glare data is shared between individual vehicles and with a remote data processing system for further analysis and action.

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

A vehicle stop position setting apparatus is provided which includes a target route calculator (102) configured to calculate a target route for a subject vehicle and a vehicle stop position detector (101) configured to detect a vehicle stop position in a particular situation. The vehicle stop position is present on the target route calculated by the target route calculator (102). The vehicle stop position setting apparatus further includes a vehicle stop position setting unit (103) configured to, when the vehicle stop position in the particular situation is detected by the vehicle stop position detector (101), set a target vehicle stop position for the subject vehicle to be in a vehicle attitude that conforms to the particular situation.