# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 3 Limitations and Advances of Road Infrastructure
### Traffic Sign Recognition System (TSRS)  
#### 3.1.1 Limitations  
TSRS is not a recent technology and has been present in the market for more than a decade. However, its performance needs to be improved, and it needs to be integrated into more advance ADAS such as intelligent speed assist (ISA) that automatically adapts the speed of a vehicle to the recognised speed limits. Taking into account that TSRS performance is bounded by sensors and algorithms, it can be expected to suffer from the same issues as a camera or poorly trained model. Therefore, we categorised limitations of TSRS into whether they were caused by environmental conditions, insufficient maintenance or design. Harsh weather, lack or excess of light, backgrounds with a similar colour as a sign, or the presence of physical obstructions are environment parameters that significantly influence the performance of TSRS due to limitations of the camera itself and obstructed FOV. Many studies indicate that the performance of ADAS has been rarely investigated under challenging weather and lighting conditions as well as the lack of databases for training algorithms for those conditions. In their survey study on traffic sign detection, tracking and classification, the authors identified variable lighting conditions as one of the main issues affecting reliable detection [29]. The reason is that detection is highly dependent on the colours of signs, whose appearance can be greatly affected by variations in illumination. Studies [52; 53] investigating the performance of the commonly used methods for TSRS are based on deep learning architecture and existing datasets regarding challenging conditions. The challenging conditions that have been considered include lens blur, dirty lens, darkening, shadow, noise, code errors, snow, rain, haze, exposure and more. The authors concluded that lens blur, exposure, codec errors and precipitation, such as rain or snow, lead to significant degradation in performance of the commonly used methods. A recent study [54] indicates the impact of rain on object detection has not been sufficiently studied. Nevertheless, the authors highlighted that one of the most challenging tasks is to isolate raindrops and restore the information in scenes occluded by rain. Despite some inclement weather, not all signs have the same detection rate. Signs with a specific shape, such as yield or stop, are very well detected and recognised, while speed limits, which have numerals that need to be recognised, are slightly more challenging [55]. However, the same study showed that, to some extent, the performance of TSRS can be improved by training the models on a bigger set, even for inclement weather. Another significant parameter is reflectivity, especially during low visibility conditions. A recent study showed that signs of a higher retroreflection class experience improved detection and readability by TSRS. Different retroreflective materials (sheeting) return different percentages of light back to the source (drivers/camera) and thus impact the overall night-time visibility of signs [55]. With the exception of retroreflection, studies have indicated that even small changes in the appearance of signs, caused by damage, or by their irregular positioning may lead to problems and failures in TSRS detection [11; 56]. Electronic signs present another hurdle for ADAS, and their refresh rates are calibrated for the human eye. Often, when seen through the camera, electronic signs appear to flicker. However, this is not always the case, and some electronic signs are well recognised, so this is more a problem regarding sign manufacturers and standards that do not take ADAS into account when it comes to their refresh rates [11]. Another common issue is collocated signs with multiple meanings, such as signs on moving buses and trucks, weather, time or vehicle type-dependent signs as well as signs with additional text.