Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm²) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10–20 mm/h), weak fog (<150 m), intense rain (30–40 mm/h), and thick fog (≤50 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30–40 mm/h) and thick fog (<50 m). Aluminum and steel showed non-observation for distances of 20–30 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation.

The performance of LiDAR sensors deteriorates under adverse weather conditions such as rainfall. However, few studies have empirically analyzed this phenomenon. Hence, we investigated differences in sensor data due to environmental changes (distance from objects (road signs), object material, vehicle (sensor) speed, and amount of rainfall) during LiDAR sensing of road facilities. The indicators used to verify the performance of LiDAR were numbers of point cloud (NPC) and intensity. Differences in the indicators were tested through a two-way ANOVA. First, both NPC and intensity increased with decreasing distance. Second, despite some exceptions, changes in speed did not affect the indicators. Third, the values of NPC do not differ depending on the materials and the intensity of each material followed the order aluminum > steel > plastic > wood, although exceptions were found. Fourth, with an increase in rainfall, both indicators decreased for all materials; specifically, under rainfall of 40 mm/h or more, a substantial reduction was observed. These results demonstrate that LiDAR must overcome the challenges posed by inclement weather to be applicable in the production of road facilities that improve the effectiveness of autonomous driving sensors.

Highway capacity manuals (HCM) have incorporated the effect of heavy vehicles (HVs) in the form of passenger car equivalents (PCEs) for estimating highway traffic conditions, which include the road’s slopes, the length of the road, the geographical characteristics, and the ratio of HVs to other types of vehicles on the road. The PCEs of HVs are critical for designing, operating, and evaluating highways. In order to analyze the effects of HVs on highway traffic flow, the real-time automatic vehicle classification (AVC) data were collected for three different major highways (four-lane, six-lane, and eight-lane highways, respectively) in the Seoul metropolitan area for the 2 months from August to September 2011. The purpose of this study was to examine the effect of HVs on traffic flows using real-time AVC data and to analyze the relationship among the average speed, the HV ratio, the flow rate, and the number of lanes. The findings revealed that the average speed decreased when the flow rate and HV ratio increased for the six-lane and the eight-lane highways. The average speed also decreased as the flow rate increased for the four-lane highway. However, the average speed stabilized when the HV ratio was in the range of 25–45% and recovered when the HV ratio exceeded 45%; this result implied a need to reexamine the existing methods of deriving the HV factors for the HCM.

Accurate vehicle localization must be maintained even in tunnel sections where GNSS reliability is degraded. However, conventional GNSS/INS-based localization rapidly accumulates errors in such environments, affecting lane-level decision-making and path-following stability. To address this problem, this study proposes a dedicated localization support sign for stable LiDAR observation and a point-cloud-registration-based correction algorithm. The proposed method detects a dedicated sign using a PointPillars-based detector, and the corresponding point cloud is registered to a pre-built reference map to estimate a rigid correction transform online. The sign was installed in a tunnel section of a proving ground that reproduces real-road conditions. For evaluation, the driving sequence was analyzed by separating the pre-entry section, the tunnel section before dedicated-sign recognition, and the section after dedicated-sign recognition. The proposed pipeline substantially reduced localization error after dedicated-sign recognition, compared with the GNSS/INS-only baseline. The dedicated sign also provided more stable correction than ordinary tunnel structures within the same registration pipeline. These results indicate that the proposed LiDAR-based pipeline can suppress localization drift in GNSS-degraded sections.