Road Geometry Conditions Research Articles

CULVERT DESIGN ANALYSIS USING HY-8 SOFTWARE TO DRAIN SURFACE RUNOFF DISCHARGE AT CV GUNUNG BATUJAJAR, WEST BANDUNG REGENCY, WEST JAVA, INDONESIA

CV Gunung Batujajar is a company engaged in the mining of industrial minerals in the form of andesite that applies an open-pit mining system (surface mining). Regarding the mining drainage system that is applied, there are still obstacles in dealing with drainage problems. The flow of rainwater in CV Gunung Batujajar generally flows in two places, to the large opening pit in the south with a percentage of 70% water flow and to the north by 30%. After further review, 30% of the water flow turned out to be flowing towards residents' housing and causing the water to overflow until it was flooded, thus disrupting the productivity of the population. Therefore, the authors conducted research related to the analysis of the drainage system using a culvert. In conducting the research, the authors took several types of data such as rainfall data for 10 years, field data on road geometry conditions, etc. With a planned rainfall value of 157.06 mm/day and a planned rainfall intensity of 54.45 mm/hour obtained the design runoff discharge is 0.35 m3/s. For the culvert design at the first outlet, a trapezoidal shape design is used with a base length of 0.225 m and a height of 0.2 m. As for the design at the second outlet, it is in the form of a trapezoid with a base length of 1.2 m and a height of 1.3 m. The second outlet is concluded to be more technically secure.

Analysis on autonomous vehicle detection performance according to various road geometry settings

This study investigates the probability of visibility limitation of autonomous driving system sensors under various road geometry conditions. This limited visibility can be a concern for the safety of the transportation system, while it is believed that the reliable autonomous driving system will improve traffic safety based on the enhanced detection and recognition capability of autonomous vehicle sensors. So, this study is conducted with the assumption that inadequate road geometry specifications can negatively affect the reliability of the sensor performance and ultimately increase autonomous vehicle crash risks. First, the study analyzes the forward detection range of an autonomous vehicle’s light detection system and radar sensors, which are affected by road curvature and slope. Second, the forward detection range of an AV is analyzed according to the actual road geometry data of expressways, national highways, and urban roads. The findings of this study emphasizes that even though autonomous vehicle manufacturers claim that their vehicles are equipped with specific automation levels allowing these vehicles to be safely driven on a particular road category, there are certain road sections that will be difficult for autonomous vehicles to traverse because of poor road geometry. Furthermore, by estimating forward detection range relative to actual road geometry data, it is demonstrated that the performance requirement level of autonomous driving systems increases when road geometries are less complex.

Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

Modeling the impact of various variables on severity of crashes involving traffic barriers

Traffic barriers are in widespread use all around the world as measures for decreasing the severity of roadside crashes. Considering a comprehensive set of more than 160 variables, this article presents the investigation of the variables affecting the severity of crashes involving traffic barriers. An ordinal logistic regression approach was applied to 10-year crash data collected in Wyoming. The crash data included a wide range of various parameters related to traffic, drivers, and road geometry conditions. The results show that the type of vehicles and traffic barriers has the highest effect on the severity of crashes involved with traffic barriers. The rigid barrier might be the best barrier type for reducing trucks’ crash severity, whereas guardrail barrier did not provide the same level of protection for trucks on high truck traffic-volume roads. The presented analytical model is more practical for rural states similar to the Rocky Mountains Region due to its similar conditions. The conclusions of the study will help transportation agencies by (1) guiding the designers when selecting the proper type of traffic barriers based on different road conditions and (2) ranking the risk of various traffic barrier segments for prioritizing the most hazardous ones for future improvements.

Generation of a Precise Roadway Map for Autonomous Cars

This paper proposes a map generation algorithm for a precise roadway map designed for autonomous cars. The roadway map generation algorithm is composed of three steps, namely, data acquisition, data processing, and road modeling. In the data acquisition step, raw trajectory and motion data for map generation are acquired through exploration using a probe vehicle equipped with GPS and on-board sensors. The data processing step then processes the acquired trajectory and motion data into roadway geometry data. GPS trajectory data are unsuitable for direct roadway map use by autonomous cars due to signal interruptions and multipath; therefore, motion information from the on-board sensors is applied to refine the GPS trajectory data. A fixed-interval optimal smoothing theory is used for a refinement algorithm that can improve the accuracy, continuity, and reliability of road geometry data. Refined road geometry data are represented into the B-spline road model. A gradual correction algorithm is proposed to accurately represent road geometry with a reduced amount of control parameters. The developed map generation algorithm is verified and evaluated through experimental studies under various road geometry conditions. The results show that the generated roadway map is sufficiently accurate and reliable to utilize for autonomous driving.