A computing framework for addressing a variety of vehicle conditions includes receiving, from a first set of sensors by an edge compute node, first transportation network data associated with a transportation network region, receiving, from a second set of sensors by a cloud computing node, second transportation network data associated multiple transportation network regions, providing, by the edge compute node to one or more autonomous vehicles at the transportation network region, real-time transportation network region information based on at least the first transportation network data to facilitate control decisions by the one or more autonomous vehicles, and providing, by the cloud computing node to at least the one or more autonomous vehicles, non-real-time transportation network region information based on at least the second transportation network data to facilitate the control decisions by the at least one or more autonomous vehicles.

A traffic detection device for sending a call signal to a traffic signal controller, the traffic detection device having a receiver to receive information sent from a mobile device, the information including identity information and/or location information of the mobile device, a traffic control device (TCD) interface to connect the traffic detection device to the traffic signal controller, and a processor to define a dynamic approach based on one or more of the identity information, speed limit, time of day, or speed of the mobile device. The device may determine whether to send a calf signal to the traffic signal controller via the TCD interface to prompt the traffic signal controller, when the processor determines that the mobile device is in the dynamic approach.

Systems and methods are provided to implement cooperative traffic congestion detection, and enhance the accuracy of detection of traffic congestion for enhanced routing and maneuvering vehicles along a travel route. A vehicle is configured to receive vehicle data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, vehicle movement, traffic, and the like, within the vicinity along the route. The vehicle employs cooperative traffic congestion detection, and fuses data from the plurality of vehicles, including sensor-rich vehicles and legacy vehicles, and applies a learning-based algorithm, such as a machine-learning (ML) algorithm, to generate a real-time and more accurate estimate of traffic congestion.

A method of generating map data indicating a deviation from expected jam conditions on a segment of a plurality of segments in an area covered by an electronic map, the segment having associated therewith a jam probability indicating the likelihood of a jam on that segment. The method comprises establishing an expected jam condition for the segment based on the jam probability of that segment, and then obtaining live data indicating the jam conditions on at least one of the plurality of other segments in the area. A revised jam condition can then be established for the segment using the obtained live data.

A system and a method for the determination and forecast of absolute and relative risks for car accidents based on exclusively non-insurance related measuring data and based on automated traffic pattern recognition, wherein data records of accident events are generated and location-dependent probability values for specific accident conditions associated with the risk of car accident are determined. The proposed system provides a grid-based, technically new way of automation of risk-prediction related to motor accidents using environment based factors including socio-economic factors that are impacting motor traffic and are location dependent received from appropriate measuring devices and systems. In this way, predictions of the accident risk for arbitrary areas can be provided. The system is calibrated by comparing features of areas or road segments with the number and type of accidents that have measured or registered there, linking the features and accident data e.g. using the below discussed machine learning techniques.

An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a driving scenario machine learning network and providing a simulated driving environment. One of the operations is performed by receiving video data that includes multiple video frames depicting an aerial view of vehicles moving about an area. The video data is processed and driving scenario data is generated which includes information about the dynamic objects identified in the video. A machine learning network is trained using the generated driving scenario data. A 3-dimensional simulated environment is provided which is configured to allow an autonomous vehicle to interact with one or more of the dynamic objects.

This application provides an information processing method performed by a computing device. The method includes the following steps: acquiring free parking space information of a target path in a time period T1, the target path including N stop areas, the time period T1 being divided into K unit times; determining a free parking space change trend of the N stop areas in the time period T1 according to the free parking space information, and mining the free parking space change trend of the N stop areas in the time period T1 to obtain parking space prediction information; and performing traffic planning for a vehicle along the target path according to the parking space prediction information. The solution can well adapt to traffic scene requirements and improve the accuracy of traffic planning.

The present invention discloses a method and system for evaluating road safety based on multi-dimensional influencing factors, and relates to the field of road safety technologies. Based on historical traffic data and corresponding safety influencing factors, safety evaluation models in different dimensions are respectively constructed, and road safety risk exposure is classified flexibly. The safety evaluation models in macro and micro dimensions are linked by using a constraint function, and influence mechanisms of the safety influencing factors are determined respectively. Specifically, a safety evaluation model is constructed and obtained for each sub-region in a limited region range. The safety evaluation model is applied to obtain influencing factors of safety of each traffic road in the sub-region, and safety evaluation is performed on the sub-region. Through the technical solutions of the present invention, an accurate, comprehensive, objective method for evaluating road safety that reflects authentic influence data is provided, which has a wider application scope.

Methods and devices for generating data for controlling a Self-Driving Car (SDC) are disclosed. The method includes: (i) acquiring a predicted object trajectory for an object, (ii) acquiring a set of anchor points along the lane for the SDC, (iii) for each one of the set of anchor points, determining a series of future moments in time when the SDC is potentially located at the respective one of the set of anchor points, thereby generating a matrix structure including future position-time pairs, (iv) for each future position-time pair in the matrix structure, using the predicted object trajectory for determining a distance between a closest object to the SDC as if the SDC is located at the respective future position-time pair, and (v) storing the distance between the closest object to the SDC in association with the respective future position-time pair in the matrix structure.