The failure to estimate friction on the road is a significant cause of roadway departure crashes. It is even more relevant to autonomous vehicles (AVs) as they are currently not designed to measure the skid resistance of roads. Most traditional measurements are too inefficient for real-time AV control. Thus, in this paper, we propose a dynamic method to estimate pavement friction level using computer vision. We collect 100 sets of high-quality road images and their resistance values to train the model, and design two major methods for analysis: 1) texture identification, consisting of grayscale-enhanced local binary pattern and the gray-level gradient co-occurrence matrix and 2) a deep neural network based on domain knowledge (TLDKNet). We introduce two standards of classification for model training and propose three indices--underestimation error, overestimation error, and accuracy--to verify the performance of our algorithms. The results showed the great correlation between pavement texture and skid resistance. The TLDKNet yielded the best performance with an accuracy of 90.67% and only 2.67% underestimation error, showing that it is sufficiently conservative with regard to safety. Based on the proposed method of estimation, a framework for anti-skid driving control is developed regarding the car-following behavior and turning movements. The anti-skid control framework provides new insights into enhancing the AV safety performance.

Automated vehicles are rapidly emerging as the new generation of transport tools. Relevant research mainly focuses on collision avoidance, lane keeping, and coordination control to ensure driving safety and improve efficiency, with little emphasis on passenger comfort. This paper proposes a set of comfort-based velocity control strategies on the basis of driving safety. Driving comfort was evaluated by multiple accelerometers fixed inside vehicles. Power spectral density analysis and one-third octave band filtering were applied to derive the weighted root mean square acceleration as an evaluation indicator for comfort. The annoy rate model is proposed to describe individual sensitivity to vibration. Field tests were conducted to investigate interactions between pavement roughness, speed, and comfort, and multiple linear relationships were identified. Three typical processes were considered: deceleration phase, uniform speed phase and abnormal occasions. The hyperbolic tangent function was utilized to provide moderate deceleration. Model parameters were set to guarantee maximal deceleration and jerk within acceptable limits. Nonlinear programming was used to balance between rapid deceleration and bumps caused by abnormal obstacles. The proposed strategy can potentially be applied for automated vehicles to improve the perceived quality of automated driving.

Automated pavement distress detection benefits road maintenance and operation by providing the condition and location of various distress rapidly. Existing work generally relies on manual labor or specific algorithms trained by dedicated datasets, which hinders the efficiency and applicable scenarios of methods. Street view map provides interactive panoramas of a large scale of urban roadway network, and is updated in a recurrent manner by the provider. This paper proposed a deep learning method based on a pre-trained neural network architecture to identify and locate different distress in real-time. About 20,000 street view images were collected and labeled as the training dataset using the Baidu e-map. Eight types of distress are notated using Yolov3 deep learning architecture. The scale-invariant feature transform (SIFT) descriptors combined with GPS and bounding boxes were applied to judge the deterioration of the distress. A decision tree was designed to evaluate the change of the distress over some time. A typical road in Shanghai was selected to verify the effectiveness of the proposed model. The images of the road from 2015 to 2017 were collected from the street view map. The results showed that the mean average precision of the proposed algorithm is 88.37%, demonstrating the vast potential of applying this method to detect pavement distress. 43 distress were newly generated, and 49 previous distress were patched in the two years. The proposed method can assist the authorities to schedule the maintenance activities more effectively.

The International Roughness Index (IRI) is a well‐recognized standard in the field of pavement management. Many different types of devices can be used to measure the IRI, but these devices are mainly mounted on a full‐size automobile and are complicated to operate. In addition, these devices are expensive. The development of methods for IRI measurement is a prerequisite for pavement management systems and other parts of the road management industry. Based on the quarter‐car model and the vehicle vibration caused by road roughness, there is a strong correlation between the in‐car Z‐axis acceleration and the IRI. The variation of speed of the car during the measurement process has a large influence on IRI estimation. A measurement system equipped with Z‐axis accelerometers and a GPS device was developed. Using the self‐designing measurement system based on the methodology proposed in this study, we performed a small‐scale field test. We used a one‐wheel linear model and two‐wheel model to fit the variation of the Z‐axis acceleration. The test results demonstrated that the low‐cost measurement system has good accuracy and could enhance the efficiency of IRI measurement.

In this paper, a 2D-wavelet approach is proposed to characterize the pavement texture acquired from the 3D laser scanner. The texture is decomposed into nine levels (from 0.1 mm to 25.6 mm) to extract the feature at multiscale. The Relative Energy (RE) and 2D-Entropy are calculated as indicators to represent the mixture surface texture distribution properties. Through conducting the 2D-wavelet decomposition on eight types of mixtures, it is found that the decomposition results perform well in recognizing the aggregates and predicting the gradation distribution. A wearing test for 100 h is also conducted and the results reveal that the mean profile depth (MPD) is highly correlative to the energy of the macro-texture while the RE and 2D-Entropy of the micro-texture decrease during the polishing process. Moreover, the mixture surface texture variates differently in the two directions: the RE of micro-texture deteriorate faster in the y-direction (the wheel movement direction) than the x-direction. The results show that the indicators can be used to measure the pavement texture variation due to traffic wear during service life, demonstrating that the novel 2D-wavelet approach the potential to evaluate road performances, such as skid and wear resistance.

Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Laboratory-based methods were used in most previous studies related to road surface friction prediction model development which are difficult for practical implementations. Moreover, for the existing studies about data-driven method development, the time-series features of road surface friction have not been considered. Thus, to utilize the time-series features of road surface friction for predictive performance improvements, this study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model. According to the experiment results, the proposed prediction model outperformed the other baseline models in terms of three metrics. The impacts of the number of time-lags, the predicting time interval, and adding other relative variables as training inputs on predictive accuracy were investigated in this research. The findings of this study can support road maintenance strategy development, especially in winter seasons, thus mitigating the impact of inclement road conditions on traffic mobility and safety.