Abstract: A method and a system for maneuvering vehicles using adjustable ultrasound sensors is disclosed. The method includes determining at least one first position of an object relative to a vehicle using at least one high range sensor and determining for each of at least one first position of the object non-conformance with one or more object position criteria. The method further includes determining at least one second position of object relative to vehicle, when at least one first position of the object is in non-conformance with one or more object position criteria. The method further includes assigning at least one ultrasound sensor to dynamically focus on the object and maneuvering the vehicle on a trajectory plan based on the at least object attribute. Each of plurality of ultrasound sensors is deployed on rotatable mount and assigned at least one ultrasound sensor is dynamically focused by rotating on associated rotatable mount.

Abstract: Disclosed herein is method and a navigation system for dynamically controlling navigation of an autonomous vehicle. In an embodiment, method comprises determining a trajectory path, comprising a plurality of path segments including at least one curved path segment and a straight path segment, by adjusting a plurality of waypoints in a base route of the autonomous vehicle and joining the plurality of waypoints using a predefined path planning model. Thereafter, a velocity profile distribution is generated for the autonomous vehicle by determining a start terminal velocity, an end terminal velocity and acceleration of the autonomous vehicle through each of the plurality of path segments in the trajectory path. Finally, a dynamic motion command is determined and applied to the autonomous vehicle based on the velocity profile distribution for dynamically controlling the navigation of the autonomous vehicle.

Abstract: The present invention discloses a method and a system for navigating an Autonomous Ground Vehicle (AGV) using a radio signal and a vision sensor. The method comprising generating a trajectory plan for a short distance from a path plan, wherein the path plan is determined using destination location and AVG location, identifying an approximate AGV location using a radio signal-based trilateration mechanism, estimating AGV location error with respect to a road lane center by determining distance from the approximate AGV location to road boundary and road lane marking line and orientation difference between AGV orientation and road orientation, and correcting the trajectory plan by using the estimated AGV location error for navigating an AGV.

Abstract: The present disclosure relates to generating maneuvering trajectories using Machine Learning (ML) model. The ML model scores each candidate maneuvering trajectories based on a plurality of dynamic profile parameters. A candidate maneuvering trajectory from a plurality of candidate maneuvering trajectories, having a best trajectory score is selected as the final maneuvering trajectory and is provided to the vehicle for navigating according to the final maneuvering trajectory. The final maneuvering trajectory is the most ideal trajectory as it is generated using the ML model based on the plurality of dynamic profile parameters. Also, the vehicle navigation is most efficient when navigated according to the final maneuvering trajectory.

Abstract: A method and system for diagnosing autonomous vehicles is disclosed. The method includes identifying anomaly in a set of navigation parameters from a plurality of navigation parameters associated with the autonomous vehicle, such that each of the set of navigation parameters is above an associated risk threshold. The method further includes validating anomaly identified for each of the set of navigation parameters. The method includes generating a set of quality parameters for the set of navigation parameters in response to validating anomaly in the set of navigation parameters. The method further includes generating values of at least one motion parameter associated with the autonomous vehicle based on values of each of the set of quality parameters fed into a trained Artificial Intelligence (AI) model. The method includes controlling the at least one motion parameter based on the generated values.

Abstract: A system and method for testing a Light Detection and Ranging (LiDAR) sensor is disclosed. The system includes the LiDAR sensor radiating a plurality of LiDAR rays, a plurality of bars positioned at a predetermined distance from the LiDAR sensor, and a testing device. The method includes triggering a LiDAR sensor to radiate a plurality of LiDAR rays. The method further includes determining at least one intersection point at each of a plurality of bars upon intersection of at least one LiDAR ray from the plurality of LiDAR rays with each of the plurality of bars. The method further includes computing at least one operational parameter for the LiDAR sensor based on the intersection points, and determining one or more test results based on the one or more operational parameters.

Abstract: The present disclosure relates to an elevation detection system for an Autonomous Vehicle (AV) and a method for detecting elevation of surrounding of the AV. The elevation detection system includes an elevation sensor unit and a computation unit. The elevation sensor unit is configured to detect an elevation of a plurality of objects having a lower most elevation, in the surrounding of the AV to determine a boundary elevation of the road. The elevation sensor unit is vertically movable within a range of vertical positions. A Light Detection and Ranging (LIDAR) sensor unit is associated with the elevation sensor unit, to detect the surrounding of the AV, having a predefined Field of View (FoV). The computation unit determines a lower limit value of the FoV and provides it to the LIDAR sensor unit for accurately detecting obstacles in the road.

Abstract: Embodiments of the present disclosure relate to generating velocity profiles for an autonomous vehicle (101). An ECU (107) of the autonomous vehicle (101) receives road information from one or more sensors (106) associated with the autonomous vehicle (101). One or more parameters related to smooth movement of the autonomous vehicle on the road is determined from the road information. Further, a first velocity profile is produced using an AI model and a second velocity profile is produced using a hierarchical model, based on the one or more parameters. Furthermore, one of the first and the second velocity profile is selected by comparing the first and the second velocity profiles. The selected velocity profile has a lower value of velocity value compared to the other velocity profile. The selected velocity profile is provided to the autonomous vehicle (101) for navigating on the road (102) smoothly.

Abstract: Disclosed herein is a method and system for dynamically generating a secure navigation path for navigation of an autonomous vehicle. The method comprises detecting disproportional acceleration of the autonomous vehicle when the autonomous vehicle is navigating from a source point to a destination point based on a predefined trajectory plan. The method comprises determining direction values of the autonomous vehicle for reaching a secure path point in the predefined trajectory plan. Based on the determined direction values, distance values are determined. The method includes detecting position of the secure path point for navigation of the autonomous vehicle based on the determined direction values and the distance values. The present disclosure uses secure path point to realign the autonomous vehicle in the predefined trajectory plan to overcome the disproportional acceleration of the autonomous vehicle due to narrow roads, upward slope, or downward slope.

Abstract: The disclosure relates to method and system for generating trajectory plan for autonomous ground vehicles (AGV). The method includes assessing an upcoming goal position for an AGV with respect to a current position of the AGV to establish a need for preparing a trajectory plan for a critical segment of the path. The method further includes determining, upon establishing the need, a next free road region based on a hint next rest pose along the critical segment, generating a trajectory sub-plan corresponding to the next free road region by iteratively determining a set of intermediate poses between the current rest pose and the hint next rest pose, and generating the trajectory plan for the critical segment of the path by iteratively generating the trajectory sub-plans between the current rest pose and the final goal pose.

Abstract: The disclosure relates to method and system for localizing an autonomous ground vehicle (AGV). In an example, the method includes receiving a line drawing corresponding to a two-dimensional (2D) camera scene captured by a camera mounted on the AGV, determining a plurality of ground-touching corner edges based on a plurality of horizontal edges and a plurality of vertical edges in the line drawing, determining a plurality of three-dimensional (3D) points corresponding to a plurality of 2D points in each of the plurality of ground-touching corner edges based on a mapping relationship between an angular orientation of a ground touching edge of an object in real-world and in camera scene and a set of intrinsic parameters of the camera, generating 2D occupancy data by plotting the plurality of 3D points in a 2D plane, and determining a location of the AGV based on the 2D occupancy data and the mapping relationship.

Abstract: Disclosed herein is method and system for determining correctness of Lidar sensor data used for localizing autonomous vehicle. The system identifies one or more Region of Interests (ROIs) in Field of View (FOV) of Lidar sensors of autonomous vehicle along a navigation path. Each ROI includes one or more objects. Further, for each ROI, system obtains Lidar sensor data comprising one or more reflection points corresponding to the one or more objects. The system forms one or more clusters in each ROI. The system identifies a distance value between, one or more clusters projected on 2D map of environment and corresponding navigation map obstacle points, for each ROI. The system compares distance value between one or more clusters and obstacle points based on which correctness of Lidar sensor data is determined. In this manner, present disclosure provides a mechanism to detect correctness of Lidar sensor data for navigation in real-time.

Abstract: Disclosed subject matter relates to field of vehicle navigation system that performs a method of generating trajectory for navigating an autonomous vehicle. A trajectory generating system associated with autonomous vehicle detects Points of Interest (POIs) for a selected segment which is at predefined distance from current position of autonomous vehicle, in real-time. Further, features of each of the POIs are determined and first level trajectory is generated for selected segment based on features of POIs proximal to selected segment. Furthermore, the trajectory generating system generates second level trajectory for portions of first level trajectory from current position of autonomous vehicle by modifying each portion based on real-time environment data.

Abstract: A method and a system for maneuvering vehicles using adjustable ultrasound sensors is disclosed. The method includes determining at least one first position of an object relative to a vehicle using at least one high range sensor and determining for each of at least one first position of the object non-conformance with one or more object position criteria. The method further includes determining at least one second position of object relative to vehicle, when at least one first position of the object is in non-conformance with one or more object position criteria. The method further includes assigning at least one ultrasound sensor to dynamically focus on the object and maneuvering the vehicle on a trajectory plan based on the at least object attribute. Each of plurality of ultrasound sensors is deployed on rotatable mount and assigned at least one ultrasound sensor is dynamically focused by rotating on associated rotatable mount.

Abstract: This disclosure relates to method and system for validating readings of orientation sensor mounted on autonomous ground vehicle (AGV). The method may include receiving distances and angles of observation of at least two fixed objects with respect to AGV at a first position and then at a second position, calculating a first orientation and a second orientation of AGV at the first position and at the second position respectively based on the distances, the angles of observation, and coordinate positions of each of the at least two fixed objects, determining an actual change in orientation of AGV based on the first orientation and the second orientation, and validating the readings of the orientation sensor based on the actual change in orientation. The at least two fixed objects are objects with pre-identified properties in a field of view of a vision sensor mounted on AGV and on both sides of AGV.

Abstract: The present disclosure relates to a methods and systems for autonomous parking of vehicles (103). The vehicle receives an input signal for parking the vehicle in parking premises (100) comprising a plurality of parking space (102) having an elevated parking boundary indicator (101). The vehicle (103) obtains a map of the parking premises (100). Further, the vehicle (103) receives a plurality of LIDAR data points (205) of the parking premises (100) and identifies a plurality of linear patterns from the plurality of LIDAR data points (205). Thereafter, the vehicle (103) detects at least two linear patterns (401) from the plurality of linear patterns, having a predefined length and parallelly spaced apart, indicating the elevated parking boundary indicator (101) of an available parking space, where the vehicle (103) can be autonomously parked in the available parking space.

Abstract: The present disclosure relates to generating maneuvering trajectories using Machine Learning (ML) model. The ML model scores each candidate maneuvering trajectories based on a plurality of dynamic profile parameters. A candidate maneuvering trajectory from a plurality of candidate maneuvering trajectories, having a best trajectory score is selected as the final maneuvering trajectory and is provided to the vehicle for navigating according to the final maneuvering trajectory. The final maneuvering trajectory is the most ideal trajectory as it is generated using the ML model based on the plurality of dynamic profile parameters. Also, the vehicle navigation is most efficient when navigated according to the final maneuvering trajectory.

Abstract: The present invention relates to a method for safely parking an autonomous vehicle on sensor anomaly. Based on current position of the AV, an angular velocity and curvature required for the AV to reach a safe parking space may be determined, upon detecting non-working of at least one primary sensor associated with the AV. Further, one or more obstacles proximal to the AV may be detected using one or more secondary sensors attached to the AV. Furthermore, based on detection of the one or more obstacles, the AV may be navigated in a track by maintaining a safe distance from the one or more obstacles. Finally, the AV may be navigated along the determined curvature upon detecting absence of the one or more obstacles to reach the safe parking space at the edge of the road.

Abstract: The present invention relates to a method and device for navigating an autonomous vehicle. The autonomous vehicle generates a first trajectory based on a path of navigation and determines a current velocity and present load of the autonomous vehicle. Further, the autonomous vehicle measures a curvature of a curved segment in the first trajectory and matches the measured curvature of the curved segment with a plurality of curvatures of a second trajectory from a trajectory performance profile of the autonomous vehicle. The second trajectory is obtained by applying a plurality of linear and angular velocities to the autonomous vehicle on a test track. Further, the autonomous vehicle selects an optimal trajectory from the trajectory performance profile based on the matching and the current velocity and the present load of the autonomous vehicle. Further, the autonomous vehicle navigates along the optimal trajectory.

Abstract: Embodiments of present disclosure relates to method and system to accurate detection of partial visual fault in lidar sensor of moving vehicle. Initially, first line cluster data for static objects present within first predefined view of Lidar sensor is determined. Static objects are tracked until viewing angle of Lidar sensor for corresponding static object is 90°. The tracking is performed based on second line cluster data corresponding to first line cluster data for static objects present within second predefined view of Lidar sensor. During tracking, non-observation of second line cluster data corresponding to first line cluster data, for one or more static objects is detected. Angle of non-observation is determined for one or more static objects upon detection. Presence of partial visual fault for Lidar sensor is detected based on angle of non-observation, for notifying navigation system associated with moving vehicle.