In one aspect, an example method includes: (a) collecting sensor data from one or more vehicles operating within a geographic region and environmental data for the geographic region; (b) generating an accident risk model using one or more machine learning models; (c) receiving a request for navigating between a first geographic position and a second geographic position; (d) identifying attributes of one or more routes between the first geographic position and the second geographic position and through the geographic region, wherein the identified attributes of the one or more routes are based on at least the generated accident risk model; and (e) transmitting an instruction that causes a mobile computing device to display a graphical indication of: (i) the one or more routes; and (ii) the identified attributes of the one or more routes.

In one aspect, an example method includes: (a) collecting sensor data from one or more vehicles operating within a geographic region and environmental data for the geographic region; (b) generating an accident risk model using one or more machine learning models; (c) receiving a request for navigating between a first geographic position and a second geographic position; (d) identifying attributes of one or more routes between the first geographic position and the second geographic position and through the geographic region, wherein the identified attributes of the one or more routes are based on at least the generated accident risk model; and (e) transmitting an instruction that causes a mobile computing device to display a graphical indication of: (i) the one or more routes; and (ii) the identified attributes of the one or more routes.

A physical quantity sensor includes a substrate, a movable body that includes a movable drive electrode, a movable detection electrode, and a connection portion for connecting the movable drive electrode and the movable detection electrode and is allowed to vibrate along a first axis with respect to the substrate, a fixed drive electrode that is fixed to the substrate, is disposed to face the movable drive electrode, and vibrates the movable body along the first axis, and a fixed monitor electrode that is fixed to the substrate, is disposed to face the movable detection electrode and detects vibration of the movable body along the first axis.

A physical quantity sensor includes a substrate, a movable body that includes a movable drive electrode, a movable detection electrode, and a connection portion for connecting the movable drive electrode and the movable detection electrode and is allowed to vibrate along a first axis with respect to the substrate, a fixed drive electrode that is fixed to the substrate, is disposed to face the movable drive electrode, and vibrates the movable body along the first axis, and a fixed monitor electrode that is fixed to the substrate, is disposed to face the movable detection electrode and detects vibration of the movable body along the first axis.

The technology employs spatial audio information to enhance wayfinding for pickup, drop-off and in-vehicle situations. The spatial information has a directional component, and a sense of distance can also be incorporated into the audio information. Audio cues or other spatial information is provided via headphones worn by a user. The spatial audio gives the user direction information, which can help locate the vehicle. In addition, this approach can be used when the rider is in the vehicle prior to exiting. For instance, spatial audio can be provided to the rider to give them contextual information about the environment outside the vehicle prior to exiting, such as whether a bicyclist is approaching on the side they will be exiting. This contextual information can alert the rider to wait or otherwise be more situationally aware when departing the vehicle.

The technology employs spatial audio information to enhance wayfinding for pickup, drop-off and in-vehicle situations. The spatial information has a directional component, and a sense of distance can also be incorporated into the audio information. Audio cues or other spatial information is provided via headphones worn by a user. The spatial audio gives the user direction information, which can help locate the vehicle. In addition, this approach can be used when the rider is in the vehicle prior to exiting. For instance, spatial audio can be provided to the rider to give them contextual information about the environment outside the vehicle prior to exiting, such as whether a bicyclist is approaching on the side they will be exiting. This contextual information can alert the rider to wait or otherwise be more situationally aware when departing the vehicle.

This disclosure relates to method and system for providing personalized driving or navigation assistance. The method may include receiving sensory data with respect to a vehicle from a plurality of sensors and multi-channel input data with respect to one or more passengers inside the vehicle from a plurality of onboard monitoring devices, performing fusion of the sensory data and the multi-channel input data to generate multimodal fusion data, determining one or more contextual events based on the multi-modal fusion data using a machine learning model, wherein the machine learning model is trained using an incremental learning process and comprises a supervised machine learning model and an unsupervised machine learning model, analysing the one or more contextual events to generate a personalized driving recommendation, and providing the personalized driving recommendation to a driver passenger or a navigation device.

This disclosure relates to method and system for providing personalized driving or navigation assistance. The method may include receiving sensory data with respect to a vehicle from a plurality of sensors and multi-channel input data with respect to one or more passengers inside the vehicle from a plurality of onboard monitoring devices, performing fusion of the sensory data and the multi-channel input data to generate multimodal fusion data, determining one or more contextual events based on the multi-modal fusion data using a machine learning model, wherein the machine learning model is trained using an incremental learning process and comprises a supervised machine learning model and an unsupervised machine learning model, analysing the one or more contextual events to generate a personalized driving recommendation, and providing the personalized driving recommendation to a driver passenger or a navigation device.

A heavy goods vehicle includes a displacement calculator that calculates a displacement by multiplying an arc length per unit rotation angle of the outer circumference of a specified tire by the first physical quantity, a vehicle position estimator that estimates a vehicle position using the displacement, and a memory that stores a correlation between a second physical quantity corresponding to a loading weight and an arc length per predetermined rotation angle at the outer circumference of the specified tire. The displacement calculator refers to the correlation to calculate a current arc length per unit rotation angle at the outer circumference of the specified tire from the second physical quantity corresponding to the loading weight, and calculates the displacement by multiplying the first physical quantity detected by the rotation amount detector by the current arc length per unit rotation angle.

A heavy goods vehicle includes a displacement calculator that calculates a displacement by multiplying an arc length per unit rotation angle of the outer circumference of a specified tire by the first physical quantity, a vehicle position estimator that estimates a vehicle position using the displacement, and a memory that stores a correlation between a second physical quantity corresponding to a loading weight and an arc length per predetermined rotation angle at the outer circumference of the specified tire. The displacement calculator refers to the correlation to calculate a current arc length per unit rotation angle at the outer circumference of the specified tire from the second physical quantity corresponding to the loading weight, and calculates the displacement by multiplying the first physical quantity detected by the rotation amount detector by the current arc length per unit rotation angle.