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: 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: This disclosure relates to method and system for dynamically determining and seamlessly switching trajectory planner (TP) for an Autonomous Ground Vehicle (AGV). The method includes determining a derived mission at a current position of the AGV along a global path based on static environment data from a navigation map and dynamic environment data acquired by the AGV. Further, the method includes identifying one or more potential TPs from a plurality of TPs based on the derived mission. Further, the method includes calculating a weighted suitability score for each of the one or more potential TPs based on a set of TP evaluation parameters. Further, the method includes determining a new TP from the one or more potential TPs to achieve the derived mission based on the weighted suitability score for each of the one or more potential TPs and a simulated evaluation of the new TP.

Abstract: This disclosure relates to method and system for detecting and compensating for mechanical fault in autonomous ground vehicle (AGV). For each of a set of trajectory plan segments along a base path during real-time navigation of the AGV, the method may include receiving a plurality of vehicle displacement parameters along a given trajectory plan segment. and determining an optimal velocity twist of the AGV in the given trajectory plan segment using an artificial intelligence (AI) model, based on the plurality of vehicle displacement parameters and a weight of the AGV. The method may further include determining the mechanical fault in the AGV based on a comparison of an actual velocity twist of the AGV in the given trajectory plan segment and the optimal velocity twist of the AGV in the given trajectory plan segment for each of the set of trajectory plan segments.

Abstract: This disclosure relates to method and system for detecting and compensating for mechanical fault in autonomous ground vehicle (AGV). For each of a set of trajectory plan segments along a base path during real-time navigation of the AGV, the method may include receiving a plurality of vehicle displacement parameters along a given trajectory plan segment. and determining an optimal velocity twist of the AGV in the given trajectory plan segment using an artificial intelligence (AI) model, based on the plurality of vehicle displacement parameters and a weight of the AGV. The method may further include determining the mechanical fault in the AGV based on a comparison of an actual velocity twist of the AGV in the given trajectory plan segment and the optimal velocity twist of the AGV in the given trajectory plan segment for each of the set of trajectory plan segments.

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.