Lane Departure Detecting with Classification of Roadway Based on Bezier Curve Fitting Using DGPS/GIS

Lane departure warning system plays an important role in safety driving by detecting a departure from a lane that is inadvertently operated on the driving trajectory. This paper suggestion detection algorithm when a vehicle departs lane boundary using GIS based on DGPS in the whole roadways. Lane segments obtained from the GIS are calculated their relative distances based on the vehicle position. Lane segments consist of consecutive straight lines and have a steady numerical error of design. In the curved section, the numerical error is bigger due to the characteristics. Accurate information about lane segments is required to reduce errors. Bezier curves are one way to extract lane segments from a curved section. The proposed lane departure algorithm is processed in two ways according to the lane type. Firstly, roads should be classified as lane type with straight and curved sections. Intersection points (IP) algorithm can easily classify the curve segments. Classified lane segments handle arithmetic relative distances for each algorithm. The lane segment of the base boundary, which is a straight lane section, has a virtual line based on the requirements of ISO 17361. The overlap area, consisting of a curve lane section and a Bezier curve, calculates the departure distance through the continuity of the driving characteristic and determines the lane departure from the curved roadways. To verify the proposed algorithm, the lane departure test led to two lane departures on each roadway. The comparison between visual sight and the departure alarm shows the driver within 0.1 second

Intelligent robotic walker with actively controlled human interaction

In this study, we developed a robotic walker that actively controls its speed and direction of movement according to the user's gait intention. Sensor fusion between a low‐cost light detection and ranging (LiDAR) sensor and inertia measurement units (IMUs) helps determine the user's gait intention. The LiDAR determines the walking direction by detecting both knees, and the IMUs attached on each foot obtain the angular rate of the gait. The user's gait intention is given as the directional angle and the speed of movement. The two motors in the robotic walker are controlled with these two variables, which represent the user's gait intention. The estimated direction angle is verified by comparison with a Kinect sensor that detects the centroid trajectory of both the user's feet. We validated the robotic walker with an experiment by controlling it using the estimated gait intention