Given the continuous improvement in the capabilities of road vehicles to detect obstacles, the road friction coefficient is closely related to vehicular braking control, thus the detection of road surface conditions (RSC), and the level is crucial for driving safety. Non-contact technology for RSC sensing is becoming the main technological and research hotspot for RSC detection because of its fast, non-destructive, efficient, and portable characteristics and attributes. This study started with mapping the relationship between friction coefficients and RSC based on the requirement for autonomous driving. We then compared and analysed the main methods and research application status of non-contact detection schemes. In particular, the use of infrared spectroscopy is expected to be the most approachable technology path to practicality in the field of autonomous driving RSC detection owing to its high accuracy and environmental adaptability properties. We systematically analysed the technical challenges in the practical application of infrared spectroscopy road surface detection, studied the causes, and discussed feasible solutions. Finally, the application prospects and development trends of RSC detection in the fields of automatic driving and exploration robotics are presented and discussed.

In-situ measurements of the spectral information on the lunar surface are of significance to study the geological evolution of the Moon. China’s Chang’E-4 (CE-4) Yutu-2 rover has conducted several in-situ spectral explorations on the Moon. The visible and near-infrared imaging spectrometer (VNIS) onboard the rover has acquired a series of in-situ spectra of the regolith at the landing site. In general, the mineralogical research of the lunar surface relies on the accuracy of the in-situ data. However, the spectral measurements of the Yutu-2 rover may be affected by shadows and stray illumination. In this study, we analyzed 106 CE-4 VNIS spectra acquired in the first 24 lunar days of the mission and noted that six of these spectra were affected by the shadows of the rover. Therefore, a method was established to correct the effects of the rover shadow on the spectral measurements. After shadow correction, the FeO content in the affected area is corrected to 14.46 wt.%, which was similar to the result calculated in the normal regolith. Furthermore, according to the visible images, certain areas of the explored sites were noted to be unusually bright. Considering the reflectance, geometric information, and shining patterns of the multi-layer insulation (MLI), we examined the influence of the specular reflection of the MLI on the bright spot regionsd, and found that the five sets of data were likely not affected by the specular reflection of the MLI. The results indicated that the complex illumination considerably influences the in situ spectral data. This study can provide a basis to analyze the VNIS scientific data and help enhance the accuracy of interpretation of the composition at CE-4 landing sites.

Organic leafy vegetables face challenges related to potential substitution with non-organic products and vulnerability to dehydration and deterioration. To address these concerns, visible and near-infrared spectroscopy (VIS-NIR) combined with linear discriminant analysis (LDA) was employed in this study to rapidly distinguish between organic and non-organic leafy vegetables. The organic category includes organic water spinach (Ipomoea aquatica Forsskal), amaranth (Amaranthus tricolor L.), lettuce (Lactuca sativa var. ramosa Hort.), and pakchoi (Brassica rapa var. chinensis (Linnaeus) Kitamura), while the non-organic category consists of their four non-organic counterparts. Binary classification was performed on the reflectance spectra of these vegetables’ leaves and stems, respectively. Given the broad range of the VIS-NIR spectrum, stability selection (SS), random forest (RF), and analysis of variance (ANOVA) were used to evaluate the importance of the wavelengths selected by genetic algorithm (GA). According to the GA-selected wavelengths and their SS-evaluated values and locations, the significant bands for leaf spectra classification were identified as 550–910 nm and 1380–1500 nm, while 750–900 nm and 1700–1820 nm were important for stem spectra classification. Using these selected bands in the LDA classification, classification accuracies of over 95% were achieved, showcasing the effectiveness of utilizing the proposed method to rapidly identify organic leafy vegetables and the feasibility and potential of using a cost-effective spectrometer that only contains necessary bands for authenticating.