Classification of road users detected and tracked with LiDAR at intersections

Data collection is a necessary component of transportation engineering. Manual data collection methods have proven to be inefficient and limited in terms of the data required for comprehensive traffic and safety studies. Automatic methods are being introduced to characterize the transportation system more accurately and are providing more information to better understand the dynamics between road users. Video data collection is an inexpensive and widely used automated method, but the accuracy of video-based algorithms is known to be affected by obstacles and shadows and the third dimension is lost with video camera data collection. The impressive progress in sensing technologies has encouraged development of new methods for measuring the movements of road users. The Center for Road Safety at Purdue University proposed application of a LiDAR-based algorithm for tracking vehicles at intersections from a roadside location. LiDAR provides a three-dimensional characterization of the sensed environment for better detection and tracking results. The feasibility of this system was analyzed in this thesis using an evaluation methodology to determine the accuracy of the algorithm when tracking vehicles at intersections. According to the implemented method, the LiDAR-based system provides successful detection and tracking of vehicles, and its accuracy is comparable to the results provided by frame-by-frame extraction of trajectory data using video images by human observers. After supporting the suitability of the system for tracking, the second component of this thesis focused on proposing a classification methodology to discriminate between vehicles, pedestrians, and two-wheelers. Four different methodologies were applied to identify the best method for implementation. The KNN algorithm, which is capable of creating adaptive decision boundaries based on the characteristics of similar observations, provided better performance when evaluating new locations. The multinomial logit model did not allow the inclusion of collinear variables into the model. Overfitting of the training data was indicated in the classification tree and boosting methodologies and produced lower performance when the models were applied to the test data. Despite ANOVA analysis not supporting superior performance by a competitor, the objective of classifying movements at intersections under diverse conditions was achieved with the KNN algorithm and was chosen as the method to implement with the existing algorithm

Intersection Complexity and Its Influence on Human Drivers

As mixed traffic between automated vehicles and human drivers in inner city becomes more prevalent in the near future understanding and predicting drivers’ behavior is important. Additionally, there is a wide variety of inner city intersections. They can differ greatly in traffic density, visibility, number of objects and many more aspects. This difference in complexity has an influence on the behavior of human drivers at intersections. To further understand the effect of complexity we conducted a naturalistic driving field study in inner city traffic with 34 participants. We focused on unsignalized intersections because there is a greater range of possibly ambiguous situations at such intersections than compared to e.g. an intersection regulated by traffic lights. Features describing the behavior (commit distance, drop in velocity and the minimal velocity) are extracted from the driven trajectories. Additionally, we define intersection complexity by several features describing an intersection. These features include both the static (street, visible and driveable width, the visibility of the other streets and the number of trees) and the dynamic environment (entry location and turning direction, numbers of vehicles, vehicles with interaction, vehicles with priority, vehicles having to yield and pedestrians). Based on those we show that the entry location and the turning direction have a significant effect on the behavior features. Additionally, we show that the typical behavior of human drivers can be predicted by the features describing an intersection’s complexity. Finally, the feature set is reduced in dimensionality for a more condensed intersection description. For that we test reduced feature sets as well as feature sets from an autoencoder and show that prediction is feasible with them as well

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LiDAR Sensors

Pedestrian safety is critical to improving walkability in cities. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. In 2020, 6,516 pedestrians were killed in traffic crashes, representing the most deaths since 1990 (NHTSA, 2020). Approximately 15% of these occurred at signalized intersections where a variety of modes converge, leading to the increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Current detection systems for pedestrians at signalized intersections consist primarily of push buttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected, especially with older devices, and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. Smart transportation systems play a vital role in enhancing mobility and safety and provide innovative techniques to connect pedestrians, vehicles, and infrastructure. Most research on smart and connected technologies is focused on vehicles; however, there is a critical need to harness the power of these technologies to study pedestrian behavior, as pedestrians are the most vulnerable users of the transportation system. While a few studies have used location technologies to detect pedestrians, this coverage is usually small and favors people with smartphones. However, the transportation system must consider a full spectrum of pedestrians and accommodate everyone. In this research, the investigators first review the previous studies on pedestrian behavior data and sensing technologies. Then the research team developed a pedestrian behavioral data collecting system based on the emerging LiDAR sensors. The system was deployed at two signalized intersections. Two studies were conducted: (a) pedestrian behaviors study at signalized intersections, analyzing the pedestrian waiting time before crossing, generalized perception-reaction time to WALK sign and crossing speed; and (b) a novel dynamic flashing yellow arrow (D-FYA) solution to separate permissive left-turn vehicles from concurrent crossing pedestrians. The results reveal that the pedestrian behaviors may have evolved compared with the recommended behaviors in the pedestrian facility design guideline (e.g., AASHTO’s “Green Book”). The D-FYA solution was also evaluated on the cabinet-in-theloop simulation platform and the improvements were promising. The findings in this study will advance the body of knowledge on equitable traffic safety, especially for pedestrian safety in the future

Safety check in critical safety scenario for self-driving vehicles

En un Mercado emergente como es el de los coches autonómos una de las funciones esenciales es asegurar la seguridad del funcionamiento de dichos sistemas. La mayoría de los comportamientos del estado del arte son capaces de manejarse en un escenario sin anomalías. Sin embargo, las dinámicas del entorno, tales como las condiciones metereológicas o las oclusiones de los sensores, pueden comprometer el funcionamiento de estos. Para ampliar los escenarios en los cuales estos sistemas son capaces de funcionar, es necesario incluir nuevas funciones de seguirdad para una conducción segura en cualquier entorno. Esta contribución demuestra una validación para vehículos autónomos basada en las condiciones del entorno. Se propone una comprobación de los obstaculos y limitaciones de los sensores. Para ellos se define una Region de Interes (RoI). Combinando ambos conceptos se obtiene un valor cuantitativo del conocimiento de los entornos del sistema. La idea propuesta se basa en modificar el plan de actuación según dicho valor, mejorando el tiempo de reacción ante situaciones imprevistas. Los resultados de la simulación e implementación física en el coche autónomo muestran una mejora en los tiempos de reacción ante situaciones fuera del dominio operacional designado. Se considera que este proyecto resuelve una de las condiciones obligatorias para conseguir un coche autónomo con un nivel de automatización de nivel cuatro.Fully autonomous vehicles must guarantee safety. Most of state of the art behaviors can drive safely on scenarios with no anomalies. However, Dynamics, such as weather conditions or occlusions, on the operational design domain might comprise the security. For further automation we need to enlarge the workbench for the technology allowing to work safely even on those situations. We contribute with a safety validation for AV based on the conditions of the scenario. We propose a check for sensor visibility and limitations. Additionally we create a definition of a Region of Interest (RoI). Merging both data we obtain a quantitative value for environment awareness. The proposed idea is to, based on that value, modify the acting plan, improving reaction time for unforeseen. The results from simulation shows that using the proposed idea, dangerous situations can be avoided. Henceforth, the fulfillment of the derived safety assessment validation can guarantee safety of the AV. The proposed idea is to, based on that validation, modify the acting plan, improving reaction time for unforeseen and endowing the autonomous vehicle of a safety check. This update is mandatory for self driving vehicles that long to achieve a level 4 automation, as the human is no longer the responsible for safety check in unpredictable situations.Universidad de Sevilla. Máster en Ingeniería Electrónica, Robótica y Automátic

Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving

Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework allows the development of BDI agents having both explicitly programmed plans and plans learned by the agent using RL. The two kinds of plans are seamlessly integrated and can be used without differences. Here, we take autonomous driving as a case study to verify the advantages of the proposed approach and framework. The BDI agent has hard-coded plans that define high-level directions while fine-grained navigation is learned by trial and error. This approach – compared to plain RL – is encouraging as RL struggles in temporally extended planning. We defined and trained an agent able to drive in a track with an intersection, at which it has to choose the correct path to reach the assigned target. A first step towards porting the system in the real-world has been done by building a 1/10 scale racecar prototype which learned how to drive in a simple track