This paper proposes a cooperative intersection collision warning system (CICWS) model that uses fixed traffic sensors to provide warning information to drivers at unsignalized intersections in a real-time manner. The CICWS model is useful for vulnerable line-of-sight and/or traffic-violation-prone environment since it determines whether the situation is really dangerous or not. More specifically, the model is for unsignalized intersections without STOP/YIELD signs, where drivers do not tend to stop. The situation forecast model uses vehicle location, speed, and time data obtained from multiple sensors located at intersection approaches, together with obstacle position and sight distance relationship. More specifically, the model has a real-time sight-distance triangle module and a collision-time prediction module. Using a microtraffic simulator called VISSIM, the validation and evaluation of the model are performed based on different scenarios with different parameters, such as inflow volume, locations of traffic sensors, design speed, and obstacle placement. The results show that the model successfully forecasts dangerous situations up to 94.3%, which may imply the deployment of the model in such an environment where vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) communication are possible. Some limitations and a future research agenda have also been discussed.

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use.

Autonomous driving includes recognition, judgment, and control technologies, and is implemented using sensors such as cameras, LiDAR, and radar. However, recognition sensors are exposed to the outside environment and their performance may deteriorate because of the presence of substances that interfere with vision, such as dust, bird droppings, and insects, during operation. Research on sensor cleaning technology to solve this performance degradation has been limited. This study used various types and concentrations of blockage and dryness to demonstrate approaches to the evaluation of cleaning rates for selected conditions that afford satisfactory results. To determine the effectiveness of washing, the study used the following criteria: washer, 0.5 bar/s and air, 2 bar/s, with 3.5 g being used three times to test the LiDAR window. The study found that blockage, concentration, and dryness are the most important factors, and in that order. Additionally, the study compared new forms of blockage, such as those caused by dust, bird droppings, and insects, with standard dust that was used as a control to evaluate the performance of the new blockage types. The results of this study can be used to conduct various sensor cleaning tests and ensure their reliability and economic feasibility.

The attention towards hydrogen energy is growing as green energy sources are becoming more and more important over the world. the South Korean government has also implemented hydrogen economy since 2018 and established a plan to develop into a hydrogen economy leading country through fuel cell electric vehicles (FCEVs) and hydrogen fuel cells. Therefore, a preparatory process of verifying awareness and acceptance of FCEV and hydrogen energy through research is necessary. As there are issues about the safety of hydrogen energy due to several explosion accidents, we hypothesized that psychological attitudes to hydrogen energy like risk perception and familiarity would be effect on vehicle purchase behavior. In order to verify hypothesis this study analyzed using logistic regression and decision tree method, a machine learning technique for a preference model among FCEVs. The results show that hydrogen risk has a larger influence than hydrogen familiarity on the choice of FCEVs. In addition, we identified that the variables which are vehicle price, charge price, VKT(Vehicle Kilometers Traveled), distance to the charging station, gender, and age group also affect on vehicle purchase behavior. Using the decision tree algorithm in the AI technique, analysis results for vehicle preference in e-mobility (EVs, hybrid, FCEVs) show that hybrid vehicles select 46%, EVs 31%, FCEVs 23%. In addition, as a result of analyzing the influence of variables on the e-mobility Preference, the major variables were charge price, distance to the charging station, and vehicle price, in the descending order of importance.

This study is the first to develop technology to evaluate the object recognition performance of camera sensors, which are increasingly important in autonomous vehicles owing to their relatively low price, and to verify the efficiency of camera recognition algorithms in obstruction situations. To this end, the concentration and color of the blockage and the type and color of the object were set as major factors, with their effects on camera recognition performance analyzed using a camera simulator based on a virtual test drive toolkit. The results show that the blockage concentration has the largest impact on object recognition, followed in order by the object type, blockage color, and object color. As for the blockage color, black exhibited better recognition performance than gray and yellow. In addition, changes in the blockage color affected the recognition of object types, resulting in different responses to each object. Through this study, we propose a blockage-based camera recognition performance evaluation method using simulation, and we establish an algorithm evaluation environment for various manufacturers through an interface with an actual camera. By suggesting the necessity and timing of future camera lens cleaning, we provide manufacturers with technical measures to improve the cleaning timing and camera safety.

Pollutants degrade the performance of LiDAR sensors used in autonomous vehicles. Therefore, there is an urgent need to develop cleaning technology for these sensors. In this study, a solid-state LiDAR sensor was selected as a target and sprayed/dried with 2.5 g of a mixture of Arizona dust and Kaolin. To achieve optimal LiDAR cleaning performance, the washer pressure, spray time, spray angle, and target point were selected as major variables. Additionally, an optimal cleaning solution for each spray was formed via the design of experiments and optimization techniques. Model suitability was observed for the second spray through to the fourth. The cleaning rate increased with the washer pressure and spray time. The influence of these variables decreased as the number of sprays increased. The spray angle and target point exhibited no significant influence, but excellent cleaning was observed in some central areas. Verification test results were within 3% for the second through fourth sprays, indicating reliability. This study used a designed experiment with 30 scenarios to reveal optimized conditions for protecting the sensor performance from external visibility obstructions. Disclosing the optimization method lowers the barrier for sensor cleaning manufacturers to develop their own technology, which ultimately enhances safer and more efficient autonomous driving.