Autonomous vehicles (AVs) are anticipated to supersede human drivers with an expectation of improved safety and operation. Since current infrastructure is designed based on the constraints caused by human drivers, it must be reassessed for autonomous driving compatibility. Recently, representatives from the infrastructure owners/operators (IOOs), automotive industry, and academia have advocated for new approaches to prepare roadways for the deployment of AVs over the next decade. Following these recommendations, this paper proposes a novel, simulation‐based approach for the assessment of highways "readiness" for AVs using 3D point cloud data. The proposed method uses octrees to perform volumetric queries for potential obstructions within an AV sensory field. The proposed approach is compared to a state‐of‐the‐art raycasting approach. Consequently, available sight distances and maximum safe speed limits based on road and AV characteristics are proposed. Finally, a discussion of the potential mitigation measures at the locations with limited sight distances is presented.

Producing as-built drawings is an important task in any road construction project. In fact, in an ideal situation, these drawings must be updated whenever major maintenance work takes place. Unfortunately, constantly updating those drawings is not always feasible due to the amount of manual work associated with the data collection in traditional surveying practice. The increase in computing power and the advancement in technology has led many transportation agencies to consider utilizing remote sensing techniques to extract roadway design features and prepare as-builts of roads. In this note, a procedure to generate as-built drawings of vertical profiles on highways using light detection and ranging (LiDAR) point cloud data are proposed. The procedure is a multistep procedure where the road centerline of each segment is first defined, after that a best fit alignment of points along the road’s centerline is generated. A digital surface model (DSM) of the LiDAR highway is created and the centerline is relayed onto the DSM before generating the road profile. The proposed method is tested using LiDAR data collected on two highways in the province of Alberta, Canada. The profiles extracted using the proposed method are compared against vertical profiles that were generated for the same segments using data collected in GPS surveys and as-built drawings developed in manual surveys. The results show the feasibility of accurately extracting road profiles from LiDAR data. The average difference in grades estimated using the proposed method and the GPS data ranged from 0.023% to 0.061%. In fact, the proposed method was able to capture details in the road profile that were not detected using GPS data, demonstrating the value of using LiDAR for road profile extraction.

The generic nature of road design is indiscriminate to age, race, or gender, as it is implicitly assumed that there are few behavioral differences between drivers while traversing various alignment elements (e.g., horizontal curves, tangential segments, etc.). For instance, the perception reaction time required, which is based on an 85th percentile value, on a tangent section is the same as that on a horizontal curve. This suggests that current guidelines do not consider the complexity that some geometric features might induce on drivers, and consequently, there is a need to address the many considerations of diversity. In this respect, human factors should be explicitly included in design guidelines. One aspect of human factors that has received little attention in the literature is related to the mental workload. In this study, a procedure is presented to estimate the mental workload for stopping sight distance. Then, reliability analysis is conducted to compare the change in the probability of non-compliance owing to the available sight distance and based on the mental workload. By analyzing data from 12 horizontal curves in Alberta, Canada, the probability of non-compliance dropped from 9.1% to 0.7%, and a moderate correlation with collisions was found. The results of the analysis showed that incorporating mental workload into the geometric design process can improve safety performance.

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Canadian municipalities are increasingly choosing to achieve bare pavement (BP) for snow and ice control during fall/winter seasons. When a snowstorm event is forecasted, one strategy is to apply anti-icing chemicals to the pavement surface to prevent the snow and ice from forming a bond with the road surface. Such an approach facilitates a more efficient plowing operation and reduces the amount of deicing chemicals needed to achieve BP. This study assesses the safety performance of achieving BP using anti-icing compared with the traditional reactive winter road maintenance (WRM) approach on urban roads using the before-and-after Empirical Bayes technique. Results suggest that achieving BP significantly reduces all collision types and severities on midblocks with a reduction value in the range of 13.7% to 19.7%. Attaining BP at intersections was found to be very effective in reducing injury collisions with an estimated reduction of 12.5%. When sites were grouped based on a WRM priority-basis, it was found that anti-icing was effective for reducing the majority of collision types and severities at the different priority levels with reductions ranging from 8.7% to 49.83% on midblocks and between 5.37% and 13% at intersections. All reductions were statistically significant. The monetary benefits of the reductions in property-damage only and nonfatal injury collisions were estimated at 60 million Canadian dollars using a 1.92% interest rate and a 2-year service life. These findings provide unequivocal evidence that achieving BP using anti-icing can lead to significant societal safety benefits that economically translate to huge collision cost savings.