Vision-based active safety system for automatic stopping

ntelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate – obtained via a vision-based system – and the wheel-locking probability – obtained via the vehicle’s CAN bus – that generates a safe braking action. The system has been tested in a real car – a convertible Citroën C3 Pluriel – equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle’s original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings

Intelligent automatic overtaking system using vision for vehicle detection

There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking manœuvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle’s actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroën car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles – a motorbike, car, and truck – with encouraging results

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

Determining Major Causes of Highway Work Zone Accidents in Kansas

Highway work zones constitute a major safety concern for government agencies, the legislature, the highway industry, and the traveling public. Despite the efforts made by government agencies and the highway industry, there is little indication that work zone crashes are on the decline nationwide. The main reason behind this is that current safety countermeasures are not working effectively in the work zones. Lack of effective countermeasures may be due to the fact that the characteristics of work zone crashes are not well understood. The primary objective of this research was to investigate the characteristics of fatal crashes and risk factors to these crashes in the work zones so that effective countermeasures could be developed and implemented in the near future. The objective was accomplished using a four-step approach. First, literature review on previous work zone crash studies was conducted to establish a solid understanding on this issue. Second, the research team collected the crash data from the KDOT accident database and the original accident reports. A total of 157 fatal crash cases between 1992 and 2004 were examined. Third, based on the collected data, the researchers systematically examined the work zone fatal crashes using statistical analysis methods such as descriptive analyses and regression analyses. At the end of analyses, the unique crash characteristics and risk factors in the work zones were determined. Finally, improvements on work zone safety were recommended

Technologies for safe and resilient earthmoving operations: A systematic literature review

Resilience engineering relates to the ability of a system to anticipate, prepare, and respond to predicted and unpredicted disruptions. It necessitates the use of monitoring and object detection technologies to ensure system safety in excavation systems. Given the increased investment and speed of improvement in technologies, it is necessary to review the types of technology available and how they contribute to excavation system safety. A systematic literature review was conducted which identified and classified the existing monitoring and object detection technologies, and introduced essential enablers for reliable and effective monitoring and object detection systems including: 1) the application of multisensory and data fusion approaches, and 2) system-level application of technologies. This study also identified the developed functionalities for accident anticipation, prevention and response to safety hazards during excavation, as well as those that facilitate learning in the system. The existing research gaps and future direction of research have been discussed

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