Pedestrian safety has become a critical issue due to the increase in pedestrian crashes every year, while proactive traffic safety management based on surrogate safety measures (SSMs) has been considered one of the key approaches to improving pedestrian safety. However, existing SSMs are developed based on the assumption that road users will maintain constant speed and direction. Risk estimations based on this assumption are less stable and more likely to be exaggerated. Considering the limitations of existing SSMs, this study has proposed a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework works by predicting the trajectories of vehicles and pedestrians using Gaussian process regression models and incorporating these results with the probability of vehicles making different maneuvers. The proposed framework has been evaluated using both simulated and real-world data collected at an intersection. The simulation results validated an increased estimated risk given time-critical pedestrian-vehicle conflicts, as well as a higher probability of the vehicle maneuver that led to such conflicts. This observation remained even when multiple conflicts arose from different directions. Moreover, experimental results using real-world data suggested that the proposed framework outperformed traditional time-to-collision (TTC) in terms of conflict prediction, quantification, and localization. For example, the proposed framework had a sensitivity of 0.92 in terms of conflict prediction, while TTC had a sensitivity of 0.62. Furthermore, the proposed framework required much less computation time compared to deep learning methods, which made it an optimal choice for proactive pedestrian safety solutions at intersections.

Advanced driving assistance systems (ADAS) are designed to reduce potential crash risks and enhance driving safety. However, drivers’ interactions with ADAS may vary depending on their individual driving styles and characteristics. This study proposes a novel approach to classifying driving styles and explores how age and gender affect interactions with ADAS. The study utilized two naturalistic driving data sets comprising 148 drivers from four age groups: teens; younger adults; middle-aged adults; and older adults. Data were collected during two periods: baseline (without ADAS); and treatment (with ADAS). First, the K-means clustering algorithm was employed to divide trips into one conservative and two aggressive groups based on three driving behavior metrics: tailgating; speeding; and lane-changing. The aggressive-trip ratios were then calculated for each driver during each of the two periods. The Bayesian Gaussian mixture model was applied to determine the threshold values of the aggressive-trip ratios to classify drivers as conservative, moderate, or aggressive during each period. This allowed for identifying changes in driving style upon the activation of ADAS. The subsequent multinomial logistic regression model results showed that driving styles vary across age groups, with teens being the most aggressive drivers. Certain changes in driving style were observed, with some conservative drivers becoming aggressive or moderate and some aggressive drivers becoming conservative or moderate, but these differences were statistically non-significant. The findings of this study indicate that warning-based ADAS may not elicit significant changes in driving style, particularly among teenage drivers who are consistently the most aggressive drivers.

Pose estimation is crucial for ensuring passenger safety and better user experiences in semi- and fully autonomous vehicles. Traditional methods relying on pose estimation from regular color images face significant challenges due to a lack of three-dimensional (3D) information and the sensitivity to occlusion and lighting conditions. Depth images, which are invariant to lighting issues and provide 3D information about the scene, offer a promising alternative. However, there is a lack of strong work in 3D pose estimation from such images due to the time-consuming process of annotating depth images with 3D postures. In this paper, we present a novel approach to 3D human posture estimation using depth and infrared (IR) images. Our method leverages a three-stage fine-tuning process involving simulation data, approximated data, and a limited set of manually annotated samples. This approach allows us to effectively train a model capable of accurate 3D pose estimation with a median error of under 10 cm across all joints, using fewer than 100 manually annotated samples. To the best of our knowledge, this is the first work focusing on vehicle occupant posture detection utilizing only depth and IR data. Our results demonstrate the feasibility and efficacy of this approach, paving the way for enhanced passenger safety in autonomous vehicle systems.

We examine the problem of analytically estimating uncertainty of objects captured using infrastructure based camera sensing. Specifically, we aim to derive a closed form relationship is established between the ground coordinates and the sources of the camera errors. Using the error propagation equation, the covariance of a given ground coordinate can be measured as a function of the camera errors. The uncertainty of the footprint of the bounding box can then be given as the function of all the extreme points of the object footprint. In order to calculate the uncertainty of a ground point, the typical error sizes of the error sources are required. We present a method of estimating the typical error sizes from an experiment using a static, high-precision LiDAR as the ground truth. Finally, we present a simulated case study and a real data collection of uncertainty quantification from infrastructure based camera - for simulation, we utilize CARLA to provide a sense of how the uncertainty changes across a left turn maneuver and for real data, we examine left, straight and right turn maneuvers for a GPS instrumented vehicle.

Low dimensional primitive feature extraction from LiDAR point clouds (such as planes) forms the basis of majority of LiDAR data processing tasks. A major challenge in LiDAR data analysis arises from the irregular nature of LiDAR data that forces practitioners to either regularize the data using some form of gridding or utilize a triangular mesh such as triangulated irregular network (TIN). While there have been a handful applications using LiDAR data as a connected graph, a principled treatment of utilizing graph-theoretical approach for LiDAR data modelling is still lacking. In this paper, we try to bridge this gap by utilizing graphical approach for normal estimation from LiDAR point clouds. We formulate the normal estimation problem in an optimization framework, where we find the corresponding normal vector for each LiDAR point by utilizing its nearest neighbors and simultaneously enforcing a graph smoothness assumption based on point samples. This is a non-linear constrained convex optimization problem which can then be solved using projected conjugate gradient descent to yield an unique solution. As an enhancement to our optimization problem, we also provide different weighted solutions based on the dot product of the normals and Euclidean distance between the points. In order to assess the performance of our proposed normal extraction method and weighting strategies, we first provide a detailed analysis on repeated randomly generated datasets with four different noise levels and four different tuning parameters. Finally, we benchmark our proposed method against existing state-of-the-art approaches on a large scale synthetic plane extraction dataset. The code for the proposed approach along with the simulations and benchmarking is available at https://github.com/arpan-kusari/graph-plane-extraction-simulation.