Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

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

Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithm that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives

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

69A3551747112Pedestrian 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\u2019s \u201cGreen Book\u201d). The D-FYA solution was also evaluated on the cabinet-in-the-loop 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

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria

Non-Line of Sight Test Scenario Generation for Connected Autonomous Vehicle

Connected autonomous vehicles (CAV) level 4-5 use sensors to perceive their environment. These sensors are able to detect only up to a certain range and this range can be further constrained by the presence of obstacles in its path or as a result of the geometry of the road, for example, at a junction. This is termed as a non-line of sight (NLOS) scenario where the ego vehicle (system under test) is unable to detect an oncoming dynamic object due to obstacles or the geometry of the road. A large body of work now exist which proposes methods for extending the perception horizon of CAV’s using vehicular communication and incorporating this into CAV algorithms ranging from obstacle detection to path planning and beyond. Such proposed new algorithms and entire systems needs testing and validating, which can be conducted through primarily two ways, on road testing and simulation. On-road testing can be extremely expensive and time-consuming and may not cover all possible test scenarios. Testing through simulation is inexpensive and has a better scenario space coverage. However, there is currently a dearth in simulated testing techniques that provides the environment to test technologies and algorithms developed for NLOS scenarios. This thesis puts forward a novel end-to-end framework for testing the abilities of a CAV through simulated generation of NLOS scenarios. This has been achieved through following the development process of Functional, Logical and Concrete scenarios along the V-model-based development process in ISO 26262. The process begins with the representation of the NLOS environment (including the digital environment) knowledge as a scalable ontology where Functional and Logical scenarios stand for different abstraction levels. The proposed new ontology comprises of six layers: ‘Environment’, ‘Road User’, ‘Object Type’, ‘Communication Network’, ‘Scene’ and ‘Scenario’. The ontology is modelled and validated in protégé software and exported to OWL API where the logical scenarios are generated and validated. An innumerable number of “concrete” scenarios are generated as a result of the possible combinations of the values from the domains of each concept’s attributes. This research puts forward a novel genetic- algorithm (GA) approach to search through the scenario space and filter out safety critical test scenarios. A critical NLOS scenario is one where a collision is highly likely because the ego vehicle was unable to detect an obstacle in time due to obstructions present in the line-of-sight of the sensors or created due to the road geometry. The metric proposed to identify critical scenarios which also acts as the GA’s fitness function uses the time-to-collision (TTC) and total stopping time (TST) metric. These generated critical scenarios and proposed fitness function have been validated through MATLAB simulation. Furthermore, this research incorporates the relevant knowledge of vehicle-to-vehicle (V2V) communication technologies in the proposed ontology and uses the communication layer instances in the MATLAB simulation to support the testing of the increasing number of approaches that uses communications for alerting oncoming vehicles about imminent danger, or in other word, mitigating an otherwise critical scenario

Risk Analysis Using Artificial Intelligence Algorithms to Prevent Collisions on Roadway Segments

This thesis focused on improving the risk analysis algorithms used in collision avoidance systems (CASs) designed to reduce the risk of three types of collision on roadway segments: animal-to-vehicle collisions, pedestrian-to-vehicle collisions, and pedestrian-to-pedestrian collisions. Currently available CASs use only one input indicator. This approach is limited as the CASs: apply a simple risk analysis algorithm based on a fixed threshold to identify risky situations; cannot simultaneously capture a variety of important collision contributing factors; and cannot combine multiple contributing factors into a single composite risk indicator. The goal of this thesis was to use artificial intelligence algorithms to create a composite risk indicator based on a combination of various input indicators. The thesis goal was achieved through four objectives: 1) Develop a fuzzy rule-based algorithm for a next generation roadside animal detection system; 2) Develop a fuzzy rule-based algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; 3) Develop a semi-supervised machine learning algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; and 4) Develop a risk analysis approach to evaluate physical distancing on urban sidewalks. Improvement of the existing risk analysis algorithm in objective 1 resulted in capturing driver behavior, animal behavior, and the spatial and temporal interaction between animal and vehicle. It also resulted in differentiating risk for following and leading vehicle and generating no-risk when vehicle passed from animal. Objectives 2 and 3 were part of the same CAS study. Improvement of the existing risk analysis algorithm in both objectives 2 and 3 resulted in capturing pedestrian behavior, driver behavior, the spatial and temporal interaction between pedestrian and vehicle with 94% accuracy when estimating all risk labels, and 88% success when identifying near miss collisions. Objective 4 successfully reflected the role of density and exposure time in the level of physical distancing. It could help decision-makers to select the most appropriate interventions (e.g., sidewalk expansion) for pedestrians to maintain physical distancing

Risk Analysis Using Artificial Intelligence Algorithms to Prevent Collisions on Roadway Segments

This thesis focused on improving the risk analysis algorithms used in collision avoidance systems (CASs) designed to reduce the risk of three types of collision on roadway segments: animal-to-vehicle collisions, pedestrian-to-vehicle collisions, and pedestrian-to-pedestrian collisions. Currently available CASs use only one input indicator. This approach is limited as the CASs: apply a simple risk analysis algorithm based on a fixed threshold to identify risky situations; cannot simultaneously capture a variety of important collision contributing factors; and cannot combine multiple contributing factors into a single composite risk indicator. The goal of this thesis was to use artificial intelligence algorithms to create a composite risk indicator based on a combination of various input indicators. The thesis goal was achieved through four objectives: 1) Develop a fuzzy rule-based algorithm for a next generation roadside animal detection system; 2) Develop a fuzzy rule-based algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; 3) Develop a semi-supervised machine learning algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; and 4) Develop a risk analysis approach to evaluate physical distancing on urban sidewalks. Improvement of the existing risk analysis algorithm in objective 1 resulted in capturing driver behavior, animal behavior, and the spatial and temporal interaction between animal and vehicle. It also resulted in differentiating risk for following and leading vehicle and generating no-risk when vehicle passed from animal. Objectives 2 and 3 were part of the same CAS study. Improvement of the existing risk analysis algorithm in both objectives 2 and 3 resulted in capturing pedestrian behavior, driver behavior, the spatial and temporal interaction between pedestrian and vehicle with 94% accuracy when estimating all risk labels, and 88% success when identifying near miss collisions. Objective 4 successfully reflected the role of density and exposure time in the level of physical distancing. It could help decision-makers to select the most appropriate interventions (e.g., sidewalk expansion) for pedestrians to maintain physical distancing