Truck activities are the primary source of NOx and PM2.5 emissions in urban environments. This study harnesses a vast truck GPS dataset from Chengdu, employing the International Vehicle Emissions (IVE) model to estimate freight-related emissions and utilizing Gradient Boosting Regression Trees (GBRT) for an in-depth analysis of the impact of urban built environment fac- tors—such as land use, traffic design, and density—on these emissions. It reveals significant correlations, highlighting that main road density, built-up area, industrial land ratio, and elec- tricity consumption play pivotal roles in affecting freight pollution. Specifically, regions with main road density ranging from 5890 to 9560 m/km2 exhibit a marked increase in emissions. These insights provide robust support for the formulation of urban planning and traffic man- agement strategies aimed at mitigating freight-related pollution, emphasizing the critical need to consider the urban built environment in policy-making processes.

Developing an effective evaluation metric is crucial for accurately and swiftly measuring LiDAR perception performance. One major issue is the lack of metrics that can simultaneously generate fast and accurate evaluations based on either object detection or point cloud data. In this study, we propose a novel LiDAR perception entropy metric based on the probability of vehicle grid occupancy. This metric reflects the influence of point cloud distribution on vehicle detection performance. Based on this, we also introduce a LiDAR deployment optimization model, which is solved using a differential evolution-based particle swarm optimization algorithm. A comparative experiment demonstrated that the proposed PE-VGOP offers a correlation of more than 0.98 with vehicle detection ground truth in evaluating LiDAR perception performance. Furthermore, compared to the base deployment, field experiments indicate that the proposed optimization model can significantly enhance the perception capabilities of various types of LiDARs, including RS-16, RS-32, and RS-80. Notably, it achieves a 25% increase in detection Recall for the RS-32 LiDAR.

The perception system based on roadside LiDAR is an important component for autonomous driving. However, previous studies optimizing the deployment configuration of roadside LiDAR systems were constrained by LiDAR designs that failed to provide uniform coverage of vehicle point clouds within the perception area. Our objective is to enhance the perception capability of roadside sensing systems by considering both the inherent properties of LiDAR (e.g., beam distribution) and its relationship to roadside infrastructure, particularly in terms of placement height. Firstly, we proposed an analytical model to establish a quantitative relationship between LiDAR configurations and the distribution of vehicle point clouds. We conducted a field test to generate real vehicle point clouds and validate the proposed analytical model using three types of LiDARs. Secondly, we optimized the roadside LiDAR configurations to maximize the perception entropy of vehicle point clouds. We designed a particle swarm optimization algorithm using initial configurations generated through the bisection method as prior knowledge. The use of a particle swarm optimization algorithm narrowed down the search area, thereby expediting the optimization process. A set of simulation-based experiments was conducted using the NGSIM I-80 dataset. Compared to three alternative configurations, the proposed joint optimization model showed the most significant improvement for the 16-beam and 32-beam LiDAR models, with detection Recall increasing by 65% and 63%, respectively. These improvements are 4.8 times and 3 times higher than those of the base configuration. This study provides an effective method for LiDAR manufacturers and traffic professionals to optimize roadside LiDAR systems, thereby enhancing vehicle perception capabilities.