ASCL-VILS 합동 연구실안 Advanced Vehicle 연구실은 자율주행 차량의 환경인지 시스템 및 VIL 시스템 구축 전담인 VILS Lab과 차량의 제어 시스템의 설계를 전담하는 ASCL 랩간의 합동 연구실입니다.

Research Topics

  • Self-Driving Car
  • ADAS System Evaluation
  • Vehicle Dynamic Control
  • Environment Recognition System
  • Vehicle-In-the-Loop System

Adaptive Urban Auto-driving Algorithm based on Sensor Weighted Integration Field​

We suggests a deep-learning based lane recognition algorithm for autonomous vehicles on urban road environments. On urban roads, there are various environments – straight, curve, crossroad, and diverse road marks. Moreover, in cases by object avoidance and/or overtaking, the vehicle maneuvers in various locations and orientations on the diverse roads. For the deep-learning based lane recognition algorithm, the dataset was firstly designed to evaluate the performance of urban roads in both normal and abnormal maneuvering, while the classes of straight, curve, crossroad, and road mark (e.g., arrow, diamond, speed bump and crosswalk) are classified. Next, the deep-learning network was constructed and trained by using the above dataset for the autonomous urban driving test. Spatial CNN (SCNN), implemented for the model, is suitable for strong spatial relationship which has similar continuous shape structure, by slices feature points in a layer and message passing in a specific direction between each slice. However, SCNN is not enough to run in real time. Thus, this study proposes a Sparse Spatial CNN (SSCNN), which reduces the computational steps and improves the execution speed. As a result, the proposed model showed a significant speed improvement with minimum performance degradation in the lane detection datasets. (KASA 2019-Fall Conference)

Adaptive Urban Auto-driving Algorithm based on Sensor Weighted Integration Field

We integrates data from three sensors – Vision, LiDAR, and GPS. The suggested algorithm decides critical motions of autonomous vehicle, such as acceleration, deceleration and steering angle. For flexible motion planning by the algorithm, the novel sensor integration method, named Sensor Weighted Integration Field (SWIF), was proposed to generate the safe trajectory of vehicle motion when the weighting function in SWIF is applied. Before forming SWIF, vision data is processed by Deep learning, which is called Spatial CNN (SCNN), in order to recognize the adjacent lanes to the vehicle. Then, by applied to SWIF, these adjacent lanes get a higher weighting value toward the center of the lane. Furthermore, obstacles detected from LiDAR are judged as the dangerous area where the algorithm lowers the weighting values. Finally, with SWIF applied with above whole data from sensors, the weighting function, where more significant area of interest expected to maneuver on the road is weighted higher, is able to generate the safe motion trajectory. As a result, the suggested algorithm for the flexible adaptive vehicle motion with minimized steering angle is successfully to avoid a dangerous area without lane and path departure in the presence of various factors on the urban roads, such as big trucks, buses and construction sites.(KASA 2019-Fall Conference)

Collision-Free Path Planning Algorithm: Dynamic Obstacles in Artificial Potential Field for Autonomous Vehicles

We proposes a path planning algorithm that considers the motion of dynamic moving objects for autonomous vehicles. Securing the safety of the autonomous vehicles is the critical requirement by
preventing collisions against other vehicles or pedestrians in the presence of high traffic density. However, collision-free path planning for autonomous vehicles is difficult problem because of the steering angle limits and the trajectory of movement of other objects. Thus, the proposed algorithm avoiding dynamically
moving objects, named Dynamic Artificial Potential Field (DAPF), has been modified to estimate the expected dynamic path of the vehicle. Using the modified DAPF, the collision-free path of the
autonomous vehicle is planned with non-holonomic conditions of the vehicle. This proposed algorithm shows better performance for generating collision-free paths than ones of the conventional APF-based algorithms.(KASA 2019-Fall Conference)

Smart Agricutural System: Autonomous Driving-based Transplating Machine

We investigated the self-driving performace of transplating machine by using vision-based tranplannted line estimation algorithm. The algorithm includes a series of image processing for autonomous navigating machine. RANSAC (Random Sample Consensus) algorithm is also applied to detect transplanted lines. Considering the result of experiment with the proposed algorithm, it is suggested to install the vision camera on the frontal side and 1m~1.4m high. Moreover, the shadow and the change of light affects the image processing and planted-line detection performance. Thus, the proposed planted-line detection algorithm using SVM classifier was investigated. Consequently, the proposed algorithm performance was convincing in the presence of disturbances, such as the shadow and the light change during agricultural operation. (KASA 2019-Spring Conference)