Beomseong Kim

Pedestrian/Vehicle Detection Using a 2.5-D Multi-Layer Laser Scanner

Laser scanners are widely used as the primary sensor for autonomous driving. When the commercialization of autonomous driving is considered, a 2.5-D multi-layer laser scanner is one of the best sensor options. In this paper, a new method is presented to detect pedestrians and vehicles using a 2.5-D multi-layer laser scanner. The proposed method consists of three steps: segmentation; feature extraction; and classification; this paper focuses on the last two steps. In feature extraction, new features for the multi-layer laser scanner are proposed to improve the classification performance. In classification, radial basis function additive kernel support vector machine is employed to reduce the computation time while maintaining the performance. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in many real-life situations.

Novel Collision Warning System using Neural Networks

Abstract Recently, there are many researches on active safety system of intelligent vehicle. To reduce the probability of collision caused by drivers inattention and mistakes, the active safety system gives warning or controls the vehicle toward avoiding collision. For the purpose, it is necessary to recognize and analyze circumstances around. In this paper, we will treat the problem about collision risk assessment. In general, it is difficult to calculate the collision risk before it happens. To consider the uncertainty of the situation, Monte Carlo simulation can be employed. However it takes long computation time and is not suitable for practice. In this paper, we apply neural networks to solve this problem. It efficiently computes the unseen data by training the results of Monte Carlo simulation. Furthermore, we propose the features affects the performance of the assessment. The proposed algorithm is verified by applications in various crash scenarios.Key Words : Collision Risk Assessment, Monte Calro Simulation, Neural Networks, Time-to-Collision(TTC)

Location Estimation and Obstacle tracking using Laser Scanner for Indoor Mobile Robots

This paper presents the method for location estimation with obstacle tracking method. A laser scanner is used to implement the system, and we assume that the map information is known. We matches the measurement of the laser scanner to estimate the location of the robot by using sequential monte carlo (SMC) method. After estimating the robot`s location, the pose of obstacles are detected and tracked, hence, we can predict the collision risk of them. Finally, we present the experiment results to verify the proposed method.

Robust object segmentation using a multi-layer laser scanner

The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the measurement of the surrounding environment as obtained by a multi-layer laser scanner. In the segmentation, a full set of measurements is decomposed into several segments, each representing a single object. Sometimes a ghost is detected due to the ground or fog, and the ghost has to be eliminated to ensure the stability of the system. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments show that the proposed method demonstrates good performance in many real-life situations.

Collision Risk Assessment for Pedestrians` Safety Using Neural Network

Abstract: This paper proposes a new collision risk assessment system for pedestrians’s safety. Monte Carlo Simulation (MCS) method is a one of the most popular method that rely on repeated random sampling to compute their result, and this method is also proper to get the results when it is unfeasible or impossible to compute an exact result. Nevertheless its advantages, it spends much time to calculate the result of some situation, we apply not only MCS but also Neural Networks in this problem. By Monte carlo method, we make some sample data for input of neural networks and by using this data, neural networks can be trained for computing collision probability of whole area where can be measured by sensors. By using this trained networks, we can estimate the collision probability at each positions and velocities with high speed and low error rate. Computer simulations will be shown the validity of our proposed method. Keywords: intelligent vehicle, monte carlo, neural networks, collision risk, monte carlo simulation

Rear-end Collision warning system using linear discriminant analysis

In this paper, we propose a Collision warning system for rear-end collision situation to avoid an accident. There are many complex situations in roadway. Therefore, we focus on a rear-end collision which is a common traffic accident wherein a vehicle crashes into the vehicle in front of it. The state of vehicles and the TTC are used to state features and LDA is used to project the state features into linear space which can indicate the possibility of collision. Computer simulation will be show the validity of our proposed method.

Prediction of Centerlane Violation for vehicle in opposite direction using Fuzzy Logic and Interacting Multiple Model

For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.

Recursive Probabilistic Approach to Collision Risk Assessment for Pedestrians` Safety

Abstract In this paper, we propose a collision risk assesment system. Fi rst, using Kalman Filter, we estimate the information of pedestrian, and second, we compute the collision probability using Monte Carlo Simulations(MCS) and neural network(NN). And we update the collision risk using time histor y which is called belief. Belief update consider not only output of Kalman Filter of only current time step but also output of Kalman Filter up to the first time step to current time step. The computer simulations will be shown the v alidity of our proposed method.Key Words : Probabilistic Collision Risk Assessment, Neural Network, Belief, Monte Carlo Simulation 1. 서 론 최근 자동차 기술의 발달은 자동차의 성능 향상 뿐 아니라. 운전자의 편의성을 향상시키기 위한 지능형 차량 시스템(Intelligence vehicle systems, ITS) 에 대한 연구가 함께 진행되었다. 이러한 운전자 편의성에 대한 연구는 운전자의 운전부담을 덜어주는 것을 목적으로 하고 있으며 종국적으로는 차량 주행의 안전성도 향상시키도록 되어 있다. 이러한 운전자 편의시스템의 대표적인 예로는 차선유지 지원 시스템 (lane-keeping support,), 충돌경고 및 회피시스템 (collision warning and collision avoidance), 차선변경 지원시스템 (assisted lane change), 도로신호 인식시스템, 사각 경고 시스템 (blind spot alert) 등이 이에 속한다. 이러한 지능형 차량 시스템 중에서도 특히 보행자의 안전을 위한 시스템은 차량 사고시 사망률 1위인 보행자 안전을 위해 각국에서 연구에 집중을 하기 시작하는 분야이다. 유럽의 경우, 2010년부터 보행자 안전 시스템 장착이 의무화가 예정되어있고[1], 국내에서도 2007년부터 국토해양부 주관 신차 안전도 평가에서 보행자 안전성에 대한 부분을 추가하여 시험하고 있다[2]. 특히 차량과 챠랑의 충돌 사고 경우 보다, 차량과 보행자의 충돌 사고의 경우, 보행자가 사망, 골절등과 같은 치명적인 상해를 입을 수 있기 때문에, 차량과 보행자의 사고를 미연에 예방하는 것이 가장 중요하다. 보행자와의 충돌을 사전에 방지하기 위해서는 우선 센서를 이용하여 보행자의 위치[3-5]를 파악하여야 하고, 파악된 보행자의 정보를 통해 보행자와의 충돌 확률을 예측하는 과정이 필요한데, 본 논문에서는 보행자를 보호하기 위한 보행자와 차량간 충돌 확률 계산 알고리즘을 제안한다. 칼만 필터를 통해 보행자의 위치를 계속적으로 추적하며, 추적된 보행자의 위치에 따른 충돌 확률을 신경 회로망을 이용하여 계산한다. 기존에 제안된 보행자의 위치에 따른 충돌 확률을 신경 회로망을 이용하여 계산하는 알고리즘[6]과는 달리, 추적된 보행자의 위치에 따른 충돌 확률을 belief를 이용하여 시간에 따라 갱신함으로 보행자의 충돌 확률을 예측한다. 본 논문의 구성은 다음과 같다. 2장에서는 칼만 필터를 이용한 보행자의 위치 추적과 신경 회로망을 이용한 보행자 위험도 판단 알고리즘에 대해서 설명하고 3장에서는 시간축을 이용한 보행자 위험도 판단 알고리즘을 제안한다. 4장에서는 제안된 알고리즘을 모의실험을 통하여 확인하고 5장에서 논문을 마무리한다.

Intervehicular Sensor Fusion for Situation Awareness

Abstract The intelligent vehicles equipped with sensors may have an ability to recognize the environment and make a safe and efficient decision. This paper proposes the intervehicular sensor fusion system for tracking the nearby objects. The proposed system gathers information from nearby vehicles and returns more exact and precise information about the environment. The system is implemented by hierarchical joint probabilistic data association (JPDA) filter. In the first layer, the hierarchical JPDA filter tracks vehicles first. In the second layer, the obstacles are tracked using the measurements from the vehicles. This system improves the tracking performance by tracking targets which are not seen by an ego vehicle but seen by another vehicle. To verify the effect of the system, the simulation results are given.