No abstract

Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead to degradations in the understanding of road scenes, resulting in difficult decision making. We have introduced two main contributions to the interpretation of road scenes in dense traffic environments. First, to obtain robust road scene understanding, we have designed a novel framework combining a lane tracker method integrated with a camera and a radar forward vehicle tracker system, which is especially useful in dense traffic situations. We have introduced an image template occupancy matching method with the integrated vehicle tracker that makes it possible to avoid extracting irrelevant lane features caused by forward target vehicles and road markers. Second, we present a robust multi-lane detection by a tracking algorithm that incudes adjacent lanes as well as ego lanes. We verify a comprehensive experimental evaluation with a real dataset comprised of problematic road scenarios. Experimental result shows that the proposed method is very reliable for multi-lane detection at the presented difficult situations.

Many studies using a laser scanner have been conducted in order to study the environment of vehicles in real time. The method to find the driving area using a two-dimensional lidar sensor is divided into a forward-looking lidar sensor and a downward-looking lidar sensor based on the installation method. A downward-looking lidar sensor looks at the ground, enabling it to recognize kerbs and ditches which are lower than the installation position of the sensor. However, a downward-looking lidar sensor requires pre-processing to find the road boundary. The existing sensor models cannot generate an occupancy grid map without support, as the driving area recognized through a downward-looking lidar sensor forms a circular sector shape from the sensor installation position to the road boundary. This paper proposes a road sensor model that is capable of modelling an occupancy grid. We also propose a method to generate an occupancy grid map more suitable for autonomous vehicles by presenting the occupancy grid map in curvilinear space. The proposed method was validated by an experiment at Hanyang University campus and the quantitative results obtained from that experiment. We also compared this method with three conventional sensor model methods. The experimental results show that our method performs better than the conventional methods do in terms of both visual qualities and metric qualities.

A parallel mechanism with redundancy can be regarded as a means for not only maximizing the benefits of parallel mechanisms but also overcoming their drawbacks. We proposed a novel parallel mechanism by eliminating an unnecessary degree of freedom of the configuration space. Because of redundancy, however, the solution for the inverse kinematics of the developed parallel mechanism is infinite. Therefore, we defined a cost function that can minimize the movement time to the target orientation and found the solution for the inverse kinematics by using a numerical method. In addition, we proposed a method for determining the boundary of the geometric singularity in order to avoid singularities.

No abstract