A method for determining a transportation mode acquires magnetometer and speed data from a mobile device, correlates the magnetometer to the speed data in groupings, and performs spectral analysis on the groups of magnetometer data. Energy calculated for each of a set of frequency components obtained from the spectral analysis is compared to a baseline value to generate a difference, and a transportation mode type is assigned to the vehicle based on the difference.

This system includes a roadside apparatus and a vehicle-side apparatus. The roadside apparatus includes a roadside sensor that detects a road situation, a recognizer that recognizes a traffic object from the road situation detected by the roadside sensor and converts a result of the recognition into stereotype information of the traffic object, and a transmitter that transmits and receives the stereotype information. The vehicle-side apparatus includes a data storage unit that stores data regarding a traffic object corresponding to the stereotype information, a receiver that receives the stereotype information transmitted by the roadside apparatus, and a presentation unit that presents the data stored in the data storage unit on the basis of the received stereotype information.

A method is provided for controlling a passage along a single-track roadway section, wherein confrontational meetings of vehicles moving in opposite directions of travel are avoided through speed adjustment and use of stopping facilities within the single-track roadway section. A system and a vehicle for carrying out the method are further provided.

A method is provided for controlling a passage along a single-track roadway section, wherein confrontational meetings of vehicles moving in opposite directions of travel are avoided through speed adjustment and use of stopping facilities within the single-track roadway section. A system and a vehicle for carrying out the method are further provided.

Surface-penetrating radar (SPR) is used to obtain images of a vehicle's undercarriage and employ these to identify the vehicle (or vehicle type or class). In particular, a vehicle may have a unique undercarriage SPR “signature” that remains largely stable over time, since it is not significantly affected by buildup of dirt, moisture or light debris. Even if the signature is not sufficiently differentiated from those of similar vehicles, it can be used to identify the vehicle within a class, which may be adequate for many purposes.

A controlling and warning system based on traffic conditions feedback and a method thereof are disclosed. In the controlling and warning system, a camera disposed on a rear of a vehicle body is configured to generate and transmit a rear video to a controlling host, and the controlling host identifies a vehicle object in the rear video and calculates a separation distance between the vehicle object and the vehicle body; the vehicle object is inputted into a large-sized vehicle recognition model which is built based on artificial intelligence neural network and trained completely, to recognize whether the vehicle object is a large-sized vehicle; when the vehicle object is recognized as the large-sized vehicle and the separation distance reaches to a safe distance, a warning signal is generated. Therefore, the technical effect of improving the warning immediacy of an approaching large vehicle can be achieved.

Disclosed are methods, systems, and non-transitory computer readable media that control an autonomous vehicle via at least two sensors. One aspect includes capturing an image of a scene ahead of the vehicle with a first sensor, identifying an object in the scene at a confidence level based on the image, determining the confidence level of the identifying is below a threshold, in response to the confidence level being below the threshold, directing a second sensor having a field of view smaller than the first sensor to generate a second image including a location of the identified object, further identifying the object in the scene based on the second image, controlling the vehicle based on the further identification of the object.

Disclosed are methods, systems, and non-transitory computer readable media that control an autonomous vehicle via at least two sensors. One aspect includes capturing an image of a scene ahead of the vehicle with a first sensor, identifying an object in the scene at a confidence level based on the image, determining the confidence level of the identifying is below a threshold, in response to the confidence level being below the threshold, directing a second sensor having a field of view smaller than the first sensor to generate a second image including a location of the identified object, further identifying the object in the scene based on the second image, controlling the vehicle based on the further identification of the object.

Provided is a preceding vehicle determining device including: a signal acquisition unit configured to acquire a signal from an object detection device; a border setting unit configured to set a border of a lane on which the own vehicle is traveling; a determination area setting unit configured to set a preceding vehicle determination area being an area forward of the own vehicle based on at least one piece of information out of vehicle type information, device type information, detection state information, or preceding vehicle information being information on a vehicle set as a preceding vehicle in previous processing, and based on the border set by the border setting unit; and a preceding vehicle determining unit configured to determine whether the another vehicle is to be set as the preceding vehicle based on a position of the another vehicle with respect to the preceding vehicle determination area.

A traffic intersection monitoring system and method to ensure a traffic signal is viewable by vehicles, bicyclists, and pedestrians, by measuring the color light intensity of the signal light, and monitoring the orientation of the traffic signal light. Each phase of color is timed and coordinated with traffic counters to generate a real-time status of predicted future traffic signal light color with corresponding traffic volume. The predicted status and traffic counts are published for the use of any applicable subscriber of the traffic intersection monitoring system.