Wooyong Han

High-Level Data Fusion Based Probabilistic Situation Assessment for Highly Automated Driving

A primary challenge of automated driving systems is the task of a situation assessment. This paper presents a high-level data fusion based probabilistic situation assessment method which is capable of assessing a current traffic situation and giving a recommendation about driving behaviors. The proposed method consists of two steps: high-level data fusion and probabilistic situation assessment. The high-level data fusion, designed to provide a better understating of observed situations, produces a local dynamic road map by integrating all dynamic entities with a high-precision static road map. The probabilistic situation assessment estimates threat levels of each lane as the probability of the lane state through the use of independent local experts based on the local dynamic road map. The recommendations for behavior decision are determined by filtering out noises resulting from object tracking even though a tracking module misses objects or detects wrong objects a lot, but immediately. The method is implemented in an open-source robot operating system to provide a reusable and hardware independent software platform, and verified and evaluated through in-vehicle tests on real highways in real-time operation.

Development of Lateral Control System for Autonomous Vehicle Based on Adaptive Pure Pursuit Algorithm

This paper proposes a lateral control system based on adaptive pure pursuit algorithm. The lateral control system consists of the path tracker and primitive driver. The path tracker is improved than original pure pursuit method. The original pure pursuit method is influenced by look ahead distance that is dynamically adjusted with velocity. If look ahead distance is short, tracking performance is good. But if controller has long look ahead distance, we can see cutting corner at curved path. To reduce tracking error, we applied the PI (Proportional-Integral) control theory in lateral offset. Proportional gain is constant and integral gain is a function of the road curvature. The total desired steering angle is sum of look ahead distance desired angle and lateral offset desired angle. The primitive driver controls steering actuator by using PID (Proportional-Integral-Derivative) controller. The performance of proposed path tracking algorithm is a good at curved path, but similar to original path tracking method at low curvature path.

Collision Avoidance in On-Road Environment for Autonomous Driving

Collision avoidance is an essential part in autonomous navigation. This paper proposes a collision avoidance method in on-road environment for autonomous driving. The proposed method divides a road map into six lane-level regions, assigns risk observers to corresponding regions, evaluates collision risks of situations based on risk observer distribution, and determines behaviors to deal with various collision-risky situations. Risk observers, designed to recognize and analyze situations at the level of lanes with regards to safety, return collision risks of situations. Based on the results of each risk observer, the system determines collision-free maneuvers to follow a global path. The method was implemented in robot operating system (ROS) for a reusable and hardware-independent software platform and tested in a two-phase procedure; simulation tests through a visualization tool called RViz in ROS with logged data and in-vehicle tests on the closed road with other vehicles to verify that our system operates properly on the road for autonomous driving without collisions. It successfully performed in several scenarios which can be happened in real road environment. The demonstration videos for in-vehicle tests are linked at fhttp://youtu.be/cjLcUdGg1j0, http://youtu.be/tB2rC6gMhcM.

Situation assessment and behavior decision for vehicle/driver cooperative driving in highway environments

This paper proposes an automated system with respect to situation assessment and behavior decision not only for cooperative driving between a driver and the system but also highly automated driving in highway environments. The proposed system includes three main parts: (1) high-level data fusion to produce a better understanding of the observed situation, (2) distributed reasoning based situation assessment to evaluate the current situation in the safety aspect and to recommend actions, and (3) behavior decision to determine collision-free and goal-directed maneuvers for vehicle/driver cooperative and highly automated driving in highway environments. The system is verified that it can work properly for cooperative and highly automated driving through in-vehicle tests in several scenarios similar to real highway environments.

Development of lateral control module for zone(u-turn) maneuver of vehicle/driver cooperative autonomous driving system

This paper proposes a lateral control module for zone(u-turn) maneuver of vehicle/driver cooperative autonomous driving system. The lateral control module consists of the path tracker and primitive driver. The path tracker is using the two sub path tracker according to maneuver of local path planner of the Co-Pilot agent block. And it generates target steering angle by using local path information, current vehicle position and azimuth information from the agent block. The primitive driver controls steering actuator to follow the command steering angle. We propose Stanley algorithm for generating target steering angle, and apply the yaw damping algorithm for increasing yaw stability at zone maneuver. The primitive driver controls steering actuator by using PID (Proportional-Integral-Derivative) controller and compensator. We verified the lateral control module for zone(u-turn) maneuver through experimental test by using Co-Pilot system vehicle.

Toward highly automated driving by vehicle-to-infrastructure communications

This paper presents a cooperative system by vehicle-to-infrastructure (V2I) communications that extends the range of environmental perception and improves the performance of situation awareness for highly automated driving. The paper consists of two steps: data fusion based situation awareness and distributed reasoning based situation assessment. The data fusion produces a V2I augmented map to provide a better understanding of driving situations by integrating road infrastructures with a high-precision map. The distributed reasoning evaluates a risky level of a current situation in terms of road infrastructures through the use of independent local experts which are distributed into lane-level local regions of the vehicles surroundings. The recommendations for driving behaviors are determined by the combination of results from each expert. The system is tested and evaluated through in-vehicle tests on a highway test road to verify that it can determine appropriate reactions under road hazard situations, such as black ice and construction.