The present invention relates to a method for estimating an accident risk of an autonomous driving unit. The method produces helpful results with less autonomous driving cycles. An autonomous-driving quantity, quantifying an autonomous-driving quality of the driving of the driving unit, is determined from driving values that have been determined from monitoring at least one driving parameter of the driving unit during autonomous driving. The autonomous-driving quantity is associated with a plurality of manual-driving quantities that have been determined from the same driving parameter during manual driving periods of different driving units. An autonomous-driving accident rate value is determined from accident rate values associated with those manual-driving quantities.

Methods and systems for providing vehicular safety control are described herein. In some embodiments, a system of vehicular safety control can help reduce or avoid human, animal, property, monetary, time and/or energy losses. The system comprises or uses sensors to perceive driving environments, and analyses of guidance commands and sensor data can evaluate potential risks. In general, implementations may include a computer-based method for controlling a vehicle, the method comprising: (a) receiving sensor data; (b) receiving a guidance command; (c) analyzing the sensor data and the guidance command, wherein the analysis comprises assessing a potential risk; and generating a control signal, wherein (1) when a potential risk is not detected, generating a control signal comprises converting the guidance command into the control signal, and (2) when a potential risk is detected, generating a control signal comprises modifying the guidance command and converting a modified guidance command into the control signal.

Embodiments of the present disclosure include automated guided vehicles (AGVs) having a broadcasting system. In one embodiment, the self-driving system includes a body having one or more motorized wheels, a console coupled in an upright position to an end of the body, and a broadcasting system disposed at the console and is operable to send a notification to one or more mobile devices, wherein the broadcasting system uses a first type of positioning system and the one or more mobile devices use a second type of positioning system different from the first type of positioning system.

This document describes techniques for localizing a vehicle to map data, for example, to enable assisted driving and automated driving within a road. A road model builder is described that uses vehicle location data to determine its position relative to map data obtained from a provider, which can be different than the location data's source. Using the vehicle position, the road model builder maintains a current lane segment for the vehicle and its relative position within the current lane segment. Based on its relative position, the road model builder determines whether a transition to another lane segment is appropriate before setting the current lane segment to be the other lane segment. The current lane segment, when provided as an input to an assisted-driving or automated-driving system, can enable safer operation of the vehicle and use fewer computing resources than other systems.

A remote operation system includes a processor that is configured to acquire position information and communication quality information from each of plural vehicles on a regular basis, create position-specific communication quality information in which the position information and the communication quality information are associated with each other, supply the position-specific communication quality information to an operator of a remote operation target vehicle.

Systems and methods of using a common control scheme to autonomously controlling a vehicle during semi-autonomous and fully autonomous driving modes are provided. In particular, embodiments of the presently disclosed technology incorporate reference tracking for driving input and vehicle state into this common control scheme. In some embodiments, this common control scheme may be implemented using Model Predictive Control (MPC).

An autonomous driving apparatus and method for an ego vehicle that autonomously travels includes a first sensor to detect a vehicle nearby the ego vehicle, a memory to store map information, and a processor to control autonomous driving of the ego vehicle based on the nearby vehicle detected by the first sensor and the map information stored in the memory.

Methods and systems for optimizing allocation of personnel and equipment to on-highway freight movement. A network of segments and nodes are defined with relation to an existing highway system. Resources as units of work are then allocated and directed to use the network in particular ways. Improvements in equipment utilization, personnel utilization, transit time and driver at-home time are realized.

A method and apparatus for providing a human-machine interface (HMI) mode of a vehicle are provided. The method, performed by a device of vehicle, for providing a human-machine interface (HMI) mode includes, determining an occupant's state, determining an HMI mode corresponding to the occupant's state among a plurality of predefined HMI modes, and providing guidance information to the occupant in a medium-specific output scheme corresponding to the determined HMI mode.

Systems and method are provided for coordinating remote control of an autonomous vehicle by a remote transportation system. In one embodiment, a method includes: receiving, by a processor of the remote transportation system, request data requesting intervention of control of the autonomous vehicle; selecting, by the processor, an operator from a plurality of available operators; selectively assigning, by the processor, the selected operator to a task associated with the intervention based on at least one of a skill level of the selected operator, a workload of the selected operator, and an economic element of the ride associated with the selected operator; and coordinating, by the processor, control of the autonomous vehicle based on the assigned operator.