Proceed-or-stop determination apparatus, includes: camera mounted on self-driving vehicle and configured to acquire image in traveling direction of the vehicle; speed acquisition unit configured to acquire traveling speed of the vehicle; position acquisition unit configured to acquire position of the vehicle with respect to stop position corresponding to traffic light in the traveling direction; and controller. The controller is configured to perform: recognizing the traffic light at predetermined cycle based on the image; recognizing lighting state of the traffic light; and determining whether the vehicle should proceed or stop at the predetermined cycle based on the lighting state, traveling speed, and position, when the traffic light is recognized. The controller determines whether the vehicle should proceed or stop further based on previous determination result in previous cycle and the lighting state recognized in the previous cycle.

A method for autonomous driving, an electronic device and a storage medium are provided. The method may include: planning a control mode of a target vehicle through an initial planning algorithm, to obtain an initial control mode; determining a first precondition that the target vehicle in an expected traveling state satisfies a preset safety rule, the expected traveling state being an expected state of the target vehicle after the target vehicle is controlled according to the initial control mode; determining whether the first precondition is established; and if yes, controlling the target vehicle according to the initial control mode.

The disclosure generally pertains to systems and methods to classify a road based on a level of support offered by the road for autonomous driving operations. An example method may involve a computer receiving sensor data from a vehicle, the sensor data containing information about a current functional condition of a road. The computer may predict a future functional condition of the road by using a deterioration model to evaluate the sensor data. The computer may then determine a level of support offered by the road for autonomous driving operations based on the future functional condition of the road, and assign a classification to the road based on the level of support offered by the road for autonomous driving operations. The level of support offered by the road for autonomous driving operations may also be based on items such as road markings, traffic signs, traffic signals, and/or infrastructure elements.

A braking control device for controlling braking of a host vehicle. For a state in which a host vehicle is stopped in an intersection by automatic emergency braking and an oncoming vehicle is approaching in an oncoming lane, the host vehicle prohibits secondary braking, flashes a hazard lamp, and prohibits an idling stop. For a state in which it is determined in that the vehicle is stopped and it is determined in that it is safe for the vehicle to start moving, the host vehicle releases stop maintenance braking.

The invention relates to a method for an automated starting of a means of transport (80) at a light-signal system (70), comprising: recording an image of the light-signal system (70) by means of a first optical sensor (30) of the means of transport (80), determining a signaling state of the light-signal system (70) by means of the recorded image, executing an automated starting process of the means of transport (80) in response to a signaling state representing and/or announcing a travel clearance, determining a user action of a driver of the means of transport (80) for plausibility checking of the starting process, and depending on a result of the plausibility check, automatically continuing or terminating of the starting process.

A vehicle control device includes: a situation detection device that detects a situation around a periphery of a vehicle and outputs a situation detection signal based on detection results; and a processor (i) that is input with the situation detection signal when the vehicle travels in an autonomous automatic driving mode, that controls travel of the vehicle based on the situation detection signal, and (ii) that enters a prohibited state that prohibits control of the travel of the vehicle in the autonomous automatic driving mode in a prohibited area in which the travel of the vehicle in the autonomous automatic driving mode is prohibited.

An energy management system determines two or more fuel components that represent fuel consumption by a vehicle system completing a trip over one or more routes. A trip plan that designates operational settings of the vehicle system at one or more of different locations, different distances along the one or more routes, or different times is generated or modified. The trip plan is based on the fuel components. The fuel components include a delta elevation component of the one or more routes, a delta speed component of the trip, a mean drag component of the vehicle system, a curvature component of the one or more routes, a base fuel component of the vehicle system, a minimum braking component of the vehicle system, a braking auxiliaries component of the vehicle system, and/or a drag variation of the vehicle system.

A method detects presence of a multi-tone siren type in an acoustic signal. The multi-tone siren type is associated with one or more siren patterns, where each siren pattern includes a number of time patterns at corresponding frequencies. The method includes processing a number of frequency components of a frequency domain representation of the acoustic signal over time to determine a corresponding plurality of values. That processing includes determining, for each frequency component, a value characterizing a presence of a time pattern associated with at least one siren pattern. The method also includes processing the values according to the siren patterns to determine a detection result indicating whether the multi-tone siren type is present in the acoustic signal.

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling routing of an autonomous vehicles (AV) by identifying routes from a first location to a second location, identifying a target efficiency value of autonomous driving along a respective route, determining, in view of historical data for the respective route and using one or more randomized conditions, a confidence level associated with the target efficiency value, selecting, based on the target efficiency values and the associated confidence level, a preferred route, and causing the AV to select the first route for travel from the first location to the second location.

A longitudinally guiding driver assistance system in a motor vehicle has a first detection system for detecting currently applying events or relevant events lying ahead, which require a change of the permissible maximum speed, a second detection system for detecting the course of the route, and a function unit which, when detecting a relevant event while taking into account the location of the relevant event, determines a location-dependent point in time, whose reaching causes the function unit to initiate an automatic adaptation of the currently permissible maximum speed or an output of a prompt information for permitting an automatic adaptation of the currently permissible maximum speed to anew permissible maximum speed. The function unit is designed to take into account available information concerning the course of the route when determining the location-dependent point in time.