The present disclosure relates to an information processing apparatus, an information processing method, and a program for ensuring safety when a line-of-sight direction of a driver is inappropriate according to a driving situation.

The line-of-sight direction of the driver of a vehicle is detected, whether or not the detected line-of-sight direction is a safe line-of-sight direction is determined, and in a case where the line-of-sight direction is determined not to be the safe line-of-sight direction, the vehicle is controlled to a safe state. The present disclosure can be applied to an in-vehicle system.

This document describes a pedestrian alert system that can draw a driver's attention to a pedestrian on or near a roadway. The described pedestrian alert system can help prevent collisions with pedestrians and other objects in poor visibility environments or when drivers may be distracted. For example, a system can determine a presence of an object in or near a travel path of a host vehicle. The system can also determine the object's position relative to the host vehicle and control a light bar to provide an indication of the object. The indication can have specific characteristics to indicate the object's position relative to the host vehicle. In this way, the described pedestrian alert system can utilize sensors to focus a driver's attention on an object before a potential crash occurs and reduce the number of traffic-related deaths.

A driver assistance system is provided with: an external sensor unit that acquires forward information; and a driver assistance control device that calculates, on the basis of the forward information, a relative speed and a predicted time to collision between the vehicle body and an object, and additionally executes a braking control for emergency avoidance that operates a brake device automatically at a timing determined on the basis of the predicted time to collision. If the object is a pedestrian, the driver assistance control device executes the braking control when the current state point specified by the relative speed and the predicted time to collision is inside an area 1 above a braking avoidance limit line L4, and executes a steering control that automatically changes the travel direction of the vehicle body when the state point is inside an area 2 at or below the limit line L4.

Described herein are various systems and processes for predictive operating assistance of vehicles. The systems and techniques described herein may be applicable to vehicles such as vehicles operated by a driver, semi-autonomous vehicles, and/or autonomous vehicles. The assistance techniques described herein may be predictive. That is, the techniques allow for the prediction of non-optimal or dangerous operating conditions before the vehicle control is compromised. Accordingly, a warning may be provided and/or operation of the vehicle may be changed based on the predictive assistance determinations. In certain embodiments, the techniques described herein may provide warnings to a driver, may detect faults within the vehicle, may aid in route planning, may detect obstacles proximate to the vehicle, and/or may aid in the operation of the vehicle.

A method, computer program product, and computing system for receiving metric data that is based, at least in part, upon sensor data generated by various sensors of an autonomous vehicle; and processing the metric data to generate a semantic understanding of the autonomous vehicle.

A computing device including interface for receiving from a sensor device sensor data representative of an environment surrounding a host vehicle, and a processor configured to obtain a planned driving action for accomplishing a navigational goal of a host vehicle operating in a first lane of a roadway, identify, from the sensor data, a moving target vehicle located in a second lane of the roadway, identify, based on the target vehicle speed and direction, the target vehicle predicted trajectory indicating a cut-in movement of the target vehicle from the second lane to the first lane, identify an intersection of a planned trajectory for the host vehicle with the predicted trajectory for the target vehicle, and determine a safety action of the host vehicle to respond to the movement of the target vehicle; and cause the safety action to be performed in the host vehicle.

The present invention provides a vehicle control device in which it is possible to modify the travel trajectory of a vehicle in response to the presence of an obstruction in the vehicle perimeter. In the present invention, the vehicle control device 1 recognizes the surroundings of the vehicle, detects a first branching point in a first route that is preset on a road, and in cases in which a prescribed condition is met by the presence of an obstruction detected from the recognized surroundings when the vehicle is to move along a travel trajectory that is based on at least one second route from among a plurality of second routes that branch from the first branching point, generates a virtual route which branches from the first route at a second branching point differing from the first branching point toward the selected second route, and modifies the travel trajectory on the basis of the generated virtual route.

An autonomous vehicle control system includes one or more processors. The one or more processors are configured to cause a transmitter to transmit a transmit signal from a laser source. The one or more processors are configured to cause a receiver to receive a return signal reflected by an object. The one or more processors are configured to cause one or more optics to generate a first polarized signal of the return signal with a first polarization, and generate a second polarized signal of the return signal with a second polarization. The one or more processors are configured to calculate a value of reflectivity based on a signal-to-noise ratio (SNR) value of the first polarized signal and an SNR value of the second polarized signal. The one or more processors are configured to operate a vehicle based on the value of reflectivity.

System, methods, and other embodiments described herein relate to accurately distinguishing a traffic light from other illuminated objects in the traffic scene and detecting states using hierarchical modeling. In one embodiment, a method includes detecting, using a machine learning (ML) model, two-dimensional (2D) coordinates of illuminated objects identified from a monocular image of a traffic scene for control adaptation by a control model. The method also includes assigning, using the ML model, computed probabilities to the illuminated objects for categories within a hierarchical ontology of environmental lights associated with the traffic scene, wherein one of the probabilities indicates existence of a traffic light instead of a brake light in the traffic scene. The method also includes executing a task by the control model for a vehicle according to the 2D coordinates and the computed probabilities.

To predict a direction in which a person will turn more accurately. A turning direction prediction system includes a processor having a leg state detection unit configured to detect whether each of left and right legs of a person is in a swing state or a stance state, a chest rotation detection unit configured to detect information about a rotation of a chest of the person around a pitch axis, a yaw axis, and a roll axis, and a direction prediction unit configured to predict a direction in which the person will turn based on the states of the left and right legs detected by the leg state detection unit and the information about the rotation of the chest detected by the chest rotation detection unit.