A system for navigation and ride hailing that provides options for navigating traffic through express lanes or the like. Information, such as express lane profile information, toll price information for the express lane during a period, and traffic information along one or more paths may be used to determine the fastest options along to a destination at different toll price points.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of an electric machine (8) of a drive train (7) of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the drive train (7) and a cost function (15) to be minimized. The cost function (15) includes a first term, a second term, and a third term. The processor unit (3) is configured for determining an input variable for the electric machine (8) by executing the MPC algorithm (13) as a function of the first, second, and third terms such that the cost function is minimized.

The present disclosure provides methods, devices and systems for route deviation quantification and vehicular route learning based thereon. In some examples, there is provided a method for route deviation quantification of a suggested route. The method comprises: obtaining a ground truth route based on a filtered trajectory, the filtered trajectory including an inferred location of origin and an inferred location of destination; obtaining a suggested route generated based on the inferred location of origin and the inferred location of destination; quantifying a deviation of the suggested route from the ground truth route by calculating an off course ratio based on a combined length of road segments in the suggested route that are matched to corresponding road segments in the ground truth route and a combined length of road segments in the ground truth route.

System and methods described herein relate to a driving speed matrix and a traffic control point matrix based on breadcrumb data. Standardized routes and times can be determined based on the breadcrumb data, and the standardized routes and times can be mapped onto a plurality of routes. Based on standardized times for routes, routes can be adjusted or optimized.

According to examples, a system for providing personalized routes may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive a user request for a personalized route wherein the user request can include at least a route origin and destination. The processor employs the user request to identify the user and retrieves the user's preferences from different recommender systems. The recommendations output by the recommender systems are ranked based on context data associated with the user request. The geographic locations associated with a predetermined number of top-ranked recommendations are obtained and a personalized route that passes through a maximum number of the geographic locations is built for a display to the user.

Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user. In some embodiments, the prediction engine only relies on user-specific data stored on the device on which this engine executes. Alternatively, in other embodiments, it relies only on user-specific data stored outside of the device by external devices/servers. In still other embodiments, the prediction engine relies on user-specific data stored both by the device and by other devices/servers.

A cloud-based, geospatially-enabled data recording, notification, and rendering system.

The present disclosure provides a method for identifying a confidence level of an autonomous driving system in an autonomous driving vehicle. The method includes determining hardware currently used by the autonomous driving system to realize required functions and environmental factors which affect the hardware capability of the hardware, and establishing a relationship between the hardware capability and the environmental factors; based on environmental factors on a future specific road section acquired in real time, calculating a degree to which the hardware capability will be affected on the specific road section; in consideration of acquired the environmental factors, judging an influence of the affected hardware capability on the realization of required functions of the autonomous driving system based on the acquired the environmental factors; and reflecting the influence on the realization of required functions of the autonomous driving system as a confidence level, and prompting the confidence level to a driver.

A user can identify the mobility dispatched to the user by changing the color of the mobility when the user utilizes a service using the mobility. Furthermore, even when the visually impaired person utilizes the mobility, a guide dog can guide the visually impaired person to the mobility through the generation of a sound that the guide dog can recognize, as well as the color, so that the visually impaired person can use the mobility conveniently.

Systems and methods relating to usage of multimodal transportation systems are disclosed. Such systems and methods may identify available local transportation modes for a user, such as vehicle-sharing, ridesharing, rental vehicles, taxicabs, owned vehicles, or public transit options. The available transportation modes may be compared, and recommendations may be presented to the user. Routes may be identified, compared, or recommended to a user, and scheduling or ticket purchasing may be facilitated. With user permission, user transportation data may be collected via a smartphone to identify user transportation patterns and preferences, thereby improving recommendations regarding and assessment of user transportation. Information regarding risks or other relevant factors associated with various transportation modes may be assessed for a user based upon typical characteristics of user transportation choices over a plurality of transportation scenarios, which may be indicated by a user transportation profile.