1,924 research outputs found

    Development of a Safety System for Intelligent Cyclist modelling

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    This paper is concerned with the modelling of cyclist road traffic crashes by considering multiple factors affecting the safety of cyclists. There are very few works in the literature dealing with such a problem. The available models in the literature are only based upon the probabilistic function of human error. In this study, we propose an intelligent safety system for modelling cycling infrastructure. The historic crash dataset for the Tyne and Wear County, north-east of England is used as a case study. There are five predictive road safety models develops using the Artificial Neural Network, with the output for the riskiest road type infrastructure. The study demonstrates that infrastructure, spatial variables, personal characteristics, and environmental conditions affect safety, which can also be used for predicting safety. These identified variables are modelled both individually and in combination with each other, and a plausible high accuracy is achieved in all the five models (> 85 accuracy). This demonstrates the benefit of using ANN for effective and efficient modelling of the safety variable for infrastructure design and planning. It is hoped that the proposed model can help in designing better cyclist infrastructure and contribute towards the development of a sustainable transportation system

    Driver interaction with vulnerable road users: Modelling driver behaviour in crossing scenarios

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    Every year, more than 5000 pedestrians and 2000 cyclists die on European roads. These vulnerable road users (VRUs) are especially at risk when interacting with cars. Intelligent safety systems (ISSs), designed to mitigate or avoid crashes between cars and VRUs, first entered the market a few years ago, and still need to be improved to be effective. Understanding how drivers interact with VRUs is crucial to improving the development and the evaluation of ISSs. Today, however, there is a lack of knowledge about driver behaviour in interactions with VRUs. To address this deficiency and contribute to realising the full potential of ISSs, this thesis has multiple objectives: 1) to investigate and describe the driver response process when a VRU crosses the driver path, 2) to devise models that can predict the driver response process, 3) to inform Euro NCAP with new knowledge about driver interactions with crossing VRUs that may guide the development of their test scenarios, and 4) to develop a framework for ISS evaluation through counterfactual simulation and analyse the impact of the chosen driver model on the simulation outcome. The thesis results show that the moment when a VRU becomes visible to the driver has the largest influence on the driver’s braking response process in driver-VRU interactions. Data gathered in driving simulators and on a test track were used to devise different predictive models: one model for the pedestrian crossing scenario, and three for the cyclist crossing scenario. The model for the pedestrian crossing scenario can estimate the moments at which key components of the driver response process (e.g. gas pedal fully released and brake onset) happen. For the cyclist crossing scenario, the first model predicts the brake onset time and the second predicts the experienced discomfort score given the cyclist appearance time. The third predicts the continuous deflection signal of the brake pedal based on the interaction of two visually-derived cues (looming and projected post-encroachment time). These models could be used to improve the design and evaluation of ISSs. From the models, appropriate warning or intervention times that are not a nuisance to the drivers could be adopted by the ISSs, therefore maximizing driver acceptance. Additionally, the models could be used in counterfactual simulations to evaluate ISS safety benefits. In fact, it was shown that driver models are a critical part of these simulations, further demonstrating the need for the development of more realistic driver models. The knowledge provided by this thesis may also guide Euro NCAP towards an improved ISS test protocol by providing information about scenarios that have not yet been evaluated

    Methods and models for safety benefit assessment of advanced driver assistance systems in car-to-cyclist conflicts

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    To help drivers avoid or mitigate the severity of crashes, advanced driver assistance systems (ADAS) can be designed to provide warnings or interventions. Prospective safety assessment of ADAS is important to quantify and optimise their safety benefit. Such safety assessment methods include, for example, virtual simulations and test-track testing.Today, there are many components of virtual safety assessment simulations with models or methods that are missing or can be substantially improved. This is particularly true for simulations assessing ADASs that address crashes involving cyclists—a crash type that is not decreasing at the same rate as the overall number of road crashes in Europe. The specific methodological gaps that this work addresses are: a) computational driver models for car-to-cyclist overtaking, b) algorithms for model fitting and efficient calculation of ADAS intervention time, and c) a method for merging data from different data sources into the safety assessment.Specifically, for a), different driver models for everyday driver behaviour while overtaking cyclists in a naturalistic driving setting were derived and compared. For b), computationally efficient algorithms to fit driver models to data and compute ADAS intervention time were developed for different types of vehicle models. The algorithms can be included in ADAS both for offline use in virtual assessment simulations and online real-time use in in-vehicle ADAS. Lastly, for c), a method was developed that uses Bayesian statistics to combine results from different data sources, e.g., simulations and test-track data, for ADAS safety benefit assessment.In addition to presenting five peer-reviewed scientific publications, which address these issues, this compilation thesis discusses the use of different data sources; introduces the fundamentals of Bayesian inference, linear programming, and numerical root-finding algorithms; and provides the rationale for methodological choices made, where relevant. Finally, this thesis describes the relationships among the publications and places them into context with existing literature.This work developed driver models for the virtual simulations and methods for the reliable estimation of the prospective safety benefit, which together have the potential to improve the design and the evaluation of ADAS in general, and ADAS for the car-to-cyclist overtaking scenario in particular

    Understanding and modelling car drivers overtaking cyclists: Toward the inclusion of driver models in virtual safety assessment of advanced driving assistance systems

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    The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. To help car drivers avoid or mitigate crashes while overtaking a cyclist, advanced driver assistance systems (ADAS) have been developed. To evaluate and further improve these ADAS to support drivers as they overtake cyclists, we need to understand and model driver behaviours.This thesis has two objectives: 1) to extract and analyse cyclist-overtaking manoeuvres from naturalistic driving data and 2) compare driver behaviour models for overtaking manoeuvres that can be used in counterfactual simulations for evaluating ADAS safety benefits.The drivers’ comfort zone boundaries (CZBs) when overtaking a cyclist were identified and analysed using naturalistic driving data. Three driver models that predict when a car driver starts steering away in order to overtake a cyclist were implemented: a threshold model, an evidence accumulation model, and a model inspired by a proportional-integral-derivative controller. These models were tested and verified using two different datasets, one from a test-track experiment and one from naturalistic driving data. Model parameters were obtained using computationally efficient linear programming.The results show that, when an oncoming vehicle was present, the drivers were significantly closer to the cyclist before steering away. This finding indicates that the presence of an oncoming vehicle is a crucial factor for the safety of the cyclist and needs to be taken into account for the development of ADAS that maintain safe distance to the cyclist. Furthermore, the quantification of the CZBs has implications for the development of ADAS which can estimate the time-to-collision to an oncoming vehicle or a cyclist to be overtaken, providing timely and acceptable warnings—or interventions—when drivers exceed their usual CZBs. A comparison of the models shows that all three are highly variable in detecting steering away time for different drivers. Furthermore, differences were discovered in detected steering away time between models fitted to test-track experiments and naturalistic driving data. Future work may focus on using larger, more diverse datasets and investigating more advanced models before including them in counterfactual simulations

    RobotCycle: Assessing Cycling Safety in Urban Environments

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    This paper introduces RobotCycle, a novel ongoing project that leverages Autonomous Vehicle (AV) research to investigate how road infrastructure influences cyclist behaviour and safety during real-world journeys. The project's requirements were defined in collaboration with key stakeholders, including city planners, cyclists, and policymakers, informing the design of risk and safety metrics and the data collection criteria. We propose a data-driven approach relying on a novel, rich dataset of diverse traffic scenes and scenarios captured using a custom-designed wearable sensing unit. By analysing road-user trajectories, we identify normal path deviations indicating potential risks or hazardous interactions related to infrastructure elements in the environment. Our analysis correlates driving profiles and trajectory patterns with local road segments, driving conditions, and road-user interactions to predict traffic behaviours and identify critical scenarios. Moreover, by leveraging advancements in AV research, the project generates detailed 3D High-Definition Maps (HD Maps), traffic flow patterns, and trajectory models to provide a comprehensive assessment and analysis of the behaviour of all traffic agents. These data can then inform the design of cyclist-friendly road infrastructure, ultimately enhancing road safety and cyclability. The project provides valuable insights for enhancing cyclist protection and advancing sustainable urban mobility.Comment: IEEE Intelligent Vehicles Symposium (IV 2024

    Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

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    Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death, because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance

    Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

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    As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated

    Towards computational models for road-user interaction: Drivers overtaking pedestrians and cyclists

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    Introduction: Crashes resulting from a failed interaction between drivers and vulnerable road users, such as pedestrians or cyclists, can lead to severe injuries or fatalities, especially after failed overtaking maneuvers on rural roads where designated refuge areas are often absent, and impact speeds high. This thesis contains two studies that shed light on driver interaction with either 1) a pedestrian or 2) a cyclist, and oncoming traffic while overtaking. Methods: The first study modeled driver behavior in pedestrian-overtaking maneuvers from naturalistic and field test data, quantifying the effect of the pedestrian’s walking direction and position, as well as the presence of oncoming traffic, on the lateral passing distance and overtaking speed. The second study modeled cyclist-overtaking maneuvers with data from a test-track experiment to quantify how the factors time gap to the oncoming traffic and cyclist lane position affect safety metrics during the maneuver and the overtaking strategy (i.e., flying or accelerative, depending on whether the driver overtook before or after the oncoming traffic had passed, respectively). Results: The results showed that, while overtaking, drivers reduced their safety margins to a pedestrian when the pedestrian was walking against the traffic direction, closer to the lane and when oncoming traffic was present. Results for cyclist overtaking were similar, showing that drivers left smaller safety margins when the cyclist rode closer to the center of the lane or when the time gap to the oncoming traffic was shorter. Under these critical conditions, drivers were more likely to opt for an accelerative maneuver than a flying one. The oncoming traffic had the most influence on drivers’ behavior among all modeling factors, in both pedestrian- and cyclist-overtaking maneuvers. Conclusion: Drivers compromised the risk of a head-on collision with the oncoming traffic by increasing the risk of rear-ending or side-swiping the pedestrian or cyclist. This thesis has implications for infrastructure design, policymaking, car assessment programs, and specifically how vehicular active safety systems may benefit from the developed models to allow more timely and yet acceptable activations
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