Abstract: A method for predictive maintenance of a component of a vehicle includes collection of first operational data, from a telematics device of the vehicle, for a set of parameters associated with the component. From the first operation data, a first dataset is selected based on an engine status of an engine of the vehicle. The first dataset is processed to obtain a plurality of feature values and the plurality of feature values are segregated into a plurality of clusters. A classifier is trained based on the plurality of clusters to determine the health status of the component. Real-time or near real-time operational data for the set of parameters is received, from the telematics device of the vehicle. The received operational data is used as an input to the trained classifier and the health status of the component is determined based on an output of the trained classifier.

Abstract: A method for facilitating vehicle servicing is provided. A first plurality of feature values corresponding to a first plurality of features are determined, for each of a plurality of vehicles. A second plurality of feature values corresponding to a second plurality of features are determined, for each of a plurality of service centers at which the plurality of vehicles were serviced. Based on the first and second pluralities of feature values, a machine learning model is trained to predict service completion times for servicing vehicles. For determining a service completion time for servicing a first vehicle at a first service center, a vehicular dataset of the first vehicle is provided as input to the machine learning model. The machine learning model predicts the service completion time based on the inputted vehicular dataset. The first vehicle is serviced at the first service center in compliance with the predicted service completion time.

Abstract: Generation of an autonomy map for assisting an autonomous vehicle includes extracting the historical autonomy information associated one or more route segments of one or more routes. An autonomy level for each route segment is determined based on the extracted historical autonomy information. A digital autonomy map is generated including each route segment of at least one route such that each route segment is tagged with the determined autonomy level. The autonomy level of each route segment is dynamically updated in real-time for providing real-time driving assistance to the autonomous vehicle.

Abstract: A method for asset health management is provided. The method includes receiving, first maintenance data, first booking data, first vehicle data, first operational data, and a labelled dataset for a set of vehicles over a first time-interval. The labelled dataset includes at least an actual health index for each vehicle observed over the first time-interval. The method includes determining a first plurality of features and a corresponding first plurality of feature values based on the first maintenance data, the first booking data, the first vehicle data, and the first operational data. The method includes training a prediction model based on the first plurality of features, the first plurality of feature values, and the labelled dataset. The method includes determining a health index of a target vehicle based on the trained first prediction model and a first dataset associated with the target vehicle.

Abstract: An authenticity detection method and system is provided for detecting an authenticity of a ride in a ride hailing environment. A set of geohashes is determined based on historical location data and a set of cell tower hashes is determined based on historical telecom data. A cellular network data corpus is generated based on the set of geohashes and the set of cell tower hashes. First location data and first telecom data are received from a driver device of a driver. A first geohash is determined based on the first location data and a first cell tower hash is determined based on the first telecom data. The authenticity of the ride is detected based on at least the cellular network data corpus, the first geohash, and the first cell tower hash.

Abstract: A method for determining a driver score for a first driver of a first vehicle. The method comprises receiving, from a database server, first maintenance data, first booking data, and first vehicle data associated with a plurality of vehicles, and first driver behavioral data for a plurality of drivers. The method includes obtaining a plurality of features and a plurality of feature values based on the first maintenance data, the first booking data, the first vehicle data, and the first driver behavioral data. The method further includes segregating the plurality of feature values into a plurality of clusters. The method includes training a classifier to determine the driver score. The method further includes receiving from the database server, a first dataset associated with the first vehicle and the first driver. The method includes determining the driver score for the first driver based on an output of the trained classifier.

Abstract: A method for vehicle maintenance at a service center is provided. A set of vehicular datasets for a set of vehicles, at a service center, is retrieved from a database. Based on the set of vehicular datasets, a service schedule for servicing the set of vehicles is determined. The service schedule is indicative of a first sequence for servicing the set of vehicles. The servicing of a first subset of the set of vehicles is initiated based on the first sequence. A progress of the servicing of the first subset of the set of vehicles is monitored based on status reports. Based on the monitored progress, the service schedule for a second subset of the set of vehicles is updated. Based on the updated service schedule, the servicing of the second subset of the set of vehicles is initiated based on a second sequence.

Abstract: A method, system, and apparatus for troubleshooting one or more multimedia devices of an audio system have been disclosed herein. An audio loopback device transmits a test signal to a first multimedia device. The first multimedia device, after receiving the test signal, generates a response signal. The audio loopback device triggers a priority controller based on an unsuccessful detection of the response signal. The priority controller troubleshoots a communicative coupling by at least changing a mode of operation of at least one of a first multimedia device or a second multimedia device from a first mode of operation to a second mode of operation. The first mode of operation or the second mode of operation may be utilized by the first multimedia device to establish the communicative coupling with the second multimedia device for delivering an audio signal of the second multimedia device on an audio device.

Abstract: A method for augmenting transport services using event detection is provided. The method includes collection of first sensor data generated by various sensors associated with a plurality of vehicles. The first sensor data includes sensor outputs that indicate a plurality of rash driving events. The sensor outputs are augmented based on angular rotation to obtain augmented sensor outputs. A prediction model is trained based on the augmented sensor outputs. Target sensor data associated with a target vehicle is provided as input to the trained prediction model, and based on an output of the trained prediction model an occurrence of a rash driving event is detected in real-time or near real-time. Based on a count of rash driving events associated with the target driver within a cumulative driving distance, a driver score of the target driver is determined.

Abstract: A vehicle allocation method and system are provided. Vehicle diagnostic data is received from vehicle-computing devices placed in vehicles. A diagnostic score is determined for each vehicle. A request for booking a ride is received from a customer device of a customer. A fare for the ride is determined for the vehicles. Options are transmitted to the customer device to select a vehicle for the ride based on the options. A customer selection is received from the customer device. A vehicle is allocated to the ride based on the customer selection.

Abstract: Allocation of a vehicle to a passenger includes receiving a ride request for a ride from a passenger device of a passenger. A ride eligibility of the passenger is determined based on health conditions of the passenger. Further, a set of drivers available for offering the ride are identified. A subset of drivers eligible for offering the ride are selected from the set of drivers based on at least the ride request and health conditions of each driver. A first driver is selected from the subset of drivers and a route is selected from a set of routes, based on the health conditions of the passenger and each driver of the subset of drivers, route conditions of each route, and the ride request. A vehicle associated with the first driver is allocated to the passenger for the ride.

Abstract: A mobile ticketing method and system for providing mobile ticketing services to users is provided. A user selects a mobile ticket and initiates payment by using a user device. In response to successful processing of the payment, the user device receives encrypted QR data from a server. The user device decrypts and format the encrypted QR data to display a QR code. The QR code is scanned at a gate terminal associated with an entry or exit gate. The gate terminal validates the QR code and broadcasts data pertaining to the QR code via a BLE network. The user device verifies the gate terminal on receiving the broadcasted data via the BLE network, and transmits a connection request to the gate terminal. The gate terminal further validates the connection request and decides whether to allow or disallow the user to pass through the entry or exit gate.

Abstract: Methods and systems for allocating vehicles for rental rides to customers are provided. A booking request for a rental ride is received from a customer device of a customer. The booking request includes at least a ride distance and a ride duration specified by the customer. Based on the ride duration in the booking request, a predefined ride distance is determined for the rental ride. A ride fare for the rental ride is determined based on at least one of the ride duration, the ride distance specified by the customer, the predefined ride distance, a base fare, and a ride cost per unit distance. Upon confirmation of the ride fare by the customer, an available vehicle is allocated to the customer for rental ride.

Abstract: Vehicle allocation method and system for ride-sharing are provided. The method includes receiving a first set of booking requests is received, at a first time instance, from a set of customer devices for sharing one or more rides. A second set of booking requests is determined, at a second time instance, based on the first set of booking requests and a third set of booking requests. The third set of booking requests is determined based on at least one of historical booking data and booking requests received after the first time instance. A set of vehicles available for the one or more rides is determined at the second time instance. A graph is generated based on the second set of booking requests and the set of available vehicles. An available vehicle is allocated to one or more customers based on optimal matching between nodes of the graph.

Abstract: Vehicle allocation methods for rental rides are provided. An online booking request for a rental ride is initiated by a passenger. Further, an available driver is identified from a set of available drivers based on one or more factors, such as a logged-in time duration of each available driver, a ride-time duration associated with the rental ride, a probability of extending the rental ride by the passenger, a destination location of each available driver, a probability of cancelling the booking request by the passenger, a current location of each available driver, a pick-up location of the passenger, a traffic stopover duration of each available driver, a number of cancelled booking requests associated with each available driver, or a driver category associated with each available driver. Thereafter, a vehicle associated with the identified driver is allocated to the passenger for the rental ride.

Abstract: Optimizing performance of an autonomous vehicle (AV) includes acquiring information pertaining to a plurality of factors associated with the AV. The plurality of factors includes a route to be traversed by the AV for a ride, a type of a road included in the route, a real-time location of the AV, a time of travel, and a weather condition at the time of travel. An optimal configuration is selected based on the acquired information for operating components of the AV. The components are configured in real time to operate at the optimal configuration. When the components operate at the optimal configuration, a power consumed by the components is reduced and a durability of the components is increased relative to when the components operate at a first configuration that is different from the optimal configuration.

Abstract: A method and a system for allocating a vehicle to a user are provided. An availability of a vehicle is detected in a geographical location for an allocation. Based on the historical travel data or a real-time travel-related intention of a set of users in the geographical area, a user is identified from the set of users. A notification message is transmitted to a user device of the identified user based on the detected availability of the vehicle in the geographical area. A user response message is received from the user device in response to the transmitted notification message. Based on the received user response message associated with the selected user, the vehicle is allocated to the selected user for a ride.

Abstract: A method for facilitating vehicle servicing is provided. A first plurality of feature values corresponding to a first plurality of features are determined, for each of a plurality of vehicles. A second plurality of feature values corresponding to a second plurality of features are determined, for each of a plurality of service centers at which the plurality of vehicles were serviced. Based on the first and second pluralities of feature values, a machine learning model is trained to predict service completion times for servicing vehicles. For determining a service completion time for servicing a first vehicle at a first service center, a vehicular dataset of the first vehicle is provided as input to the machine learning model. The machine learning model predicts the service completion time based on the inputted vehicular dataset. The first vehicle is serviced at the first service center in compliance with the predicted service completion time.

Abstract: A method for dynamic maintenance scheduling includes receiving, by a server, first maintenance data, first vehicle data, first booking data, and a plurality of maintenance plans associated with a plurality of vehicles. The plurality of maintenance plans is indicative of historical scheduled maintenance sessions of a corresponding vehicle of the plurality of vehicles. The method includes determination of a plurality of features and a corresponding plurality of feature values for each of the plurality of vehicles. The method includes training a prediction model based on the plurality of features and the corresponding feature values. The method includes determination of a maintenance criterion for a target vehicle based on the trained prediction model and a target dataset associated with the target vehicle. The maintenance criterion indicates an odometer reading range of the target vehicle during which a scheduled maintenance of the target vehicle is to be performed.

Abstract: A method for augmenting transport services using event detection is provided. The method includes collection of first sensor data generated by various sensors associated with a plurality of vehicles. The first sensor data includes sensor outputs that indicate a plurality of rash driving events. The sensor outputs are augmented based on angular rotation to obtain augmented sensor outputs. A prediction model is trained based on the augmented sensor outputs. Target sensor data associated with a target vehicle is provided as input to the trained prediction model, and based on an output of the trained prediction model an occurrence of a rash driving event is detected in real-time or near real-time. Based on a count of rash driving events associated with the target driver within a cumulative driving distance, a driver score of the target driver is determined.