## 1 Introduction  
According to the latest United Nations Population Division projections, the number of people on the planet exceeded 7.3 billion as of mid-2015 and it is expected to reach 9.7 billion in 2050 and 11.2 billion by 2100. In the next 15 years, 60% of the total population will be city dwellers. This number will increase to 70% by 2050 [1]. These results show that the population is not only increasing, but is becoming progressively concentrated in urban areas as well. In this context, public transportation will remain to be an indispensable answer to the nation's economic, energy, and environmental questions. As a result, local communities are expanding public transportation networks and people are using public transportation in increasing numbers. Urban rail is the most rational solution for public transportation because it is well placed at the core of already existing public transportation networks given its high capacity, safety, and reliability [2]. In order to improve usage of urban rail, some precautions, especially safety precautions must be taken. According to the statistics reported by [PERSON] et al. [3], 95% of traffic accident are related to human factors. Furthermore, 70% of these are directly related to human errors. There are various studies for detecting obstacles, which compare the advantages and disadvantages of different technologies. These technologies have been studied using different sensors, like laser scanners [4; 5; 6; 7; 8], radars [9; 10; 11; 12], and computer vision [13; 14; 15; 16]. Sensory fusions of the aforementioned sensors have also been studied for obtaining additional information, like laser scanner and radar fusion [17], laser scanner and computer vision fusion [18; 19; 20; 21], radar and computer vision fusion [22; 23; 24], and the fusion of all three sensors [25; 26]. Apart from these methods, digital mapping has been studied as a supplementary sensor based method for getting extra information obtained by the vehicle environment system [27]. Computer vision-based obstacle detection methods are divided into three categories. The first one is the knowledge-based approach, which uses prior knowledge of obstacle features, like color [28], symmetry [29], and shadow [30]. The second one is the stereo vision-based approach [31] and the last one is the motion-based approach [32]. Using computer vision to solve the problem of obstacle detection seems to be a rational solution at first. Although computer vision methods have advantages, such as lower cost sensors, higher information capacity, and lower operating power, all three methods fail in cases of complex shadows, complex illumination, and challenging weather conditions, like heavy rain, snow, and turbid fog. Additionally, this method needs high computational costs. All of these disadvantages put active sensors, like laser scanners and radars, to the forefront. In many applications, laser scanners and radar sensors have similar performances. Radar sensors can be separated broadly into two categories depending on the application. The first one is 77 GHz long-range radars (LRR) [33]. LRR can measure a 200 m distance like some laser scanner sensors. The second one is 24 GHz short-range radars (SRR) [34]. SRR generally operates at a shorter distance in comparison to LRR. Although radar-based methods are better in terms of detection range and reliability rate in adverse weather conditions, they have a relatively low angular resolution of \(15^{\circ}\)[35]. Thus, it is hard to detect multiple obstacles in crowded road conditions. To overcome this problem, multiple sensors need to be utilized. This makes the system more complicated. Laser scanners are widely used for many applications from bar-code reading to vibration assessment [36; 37]. Unlike video cameras, laser scanners offer accurate distances of up to 200 m. Moreover, these devices signals cannot easily interfere with other lighting sources by means of sending signals from their own signal sources.