A Quantitative Method to Determine What Collisions Are Reasonably Foreseeable and Preventable

The development of Automated Driving Systems (ADSs) has made significant progress in the last years. To enable the deployment of Automated Vehicles (AVs) equipped with such ADSs, regulations concerning the approval of these systems need to be established. In 2021, the World Forum for Harmonization of Vehicle Regulations has approved a new United Nations regulation concerning the approval of Automated Lane Keeping Systems (ALKSs). An important aspect of this regulation is that "the activated system shall not cause any collisions that are reasonably foreseeable and preventable." The phrasing of "reasonably foreseeable and preventable" might be subjected to different interpretations and, therefore, this might result in disagreements among AV developers and the authorities that are requested to approve AVs. The objective of this work is to propose a method for quantifying what is "reasonably foreseeable and preventable". The proposed method considers the Operational Design Domain (ODD) of the system and can be applied to any ODD. Having a quantitative method for determining what is reasonably foreseeable and preventable provides developers, authorities, and the users of ADSs a better understanding of the residual risks to be expected when deploying these systems in real traffic. Using our proposed method, we can estimate what collisions are reasonably foreseeable and preventable. This will help in setting requirements regarding the safety of ADSs and can lead to stronger justification for design decisions and test coverage for developing ADSs.Comment: 25 pages, 9 figures, 2 table

Scenario Extraction from a Large Real-World Dataset for the Assessment of Automated Vehicles

Many players in the automotive field support scenario-based assessment of automated vehicles (AVs), where individual traffic situations can be tested and, thus, facilitate concluding on the performance of AVs in different situations. Since a large number of different scenarios can occur in real-world traffic, the question is how to find a finite set of relevant scenarios. Scenarios extracted from large real-world datasets represent real-world traffic since real driving data is used. Extracting scenarios, however, is challenging because (1) the scenarios to be tested should assess the AVs behave safely, which conflicts with the fact that the majority of the data contains scenarios that are not interesting from a safety perspective, and (2) extensive data processing is required, which hinders the utilization of large real-world datasets. In this work, we propose an approach for extracting scenarios from real-world driving data. The first step is data preprocessing to tackle the errors and noise in real-world data by reconstructing the data. The second step performs data tagging to label actors' activities, their interactions with each other and the environment. Finally, the scenarios are extracted by searching for combinations of tags. The proposed approach is evaluated using data simulated with CARLA and applied to a part of a large real-world driving dataset, i.e., the Waymo Open Motion Dataset (WOMD). The code and scenarios extracted from WOMD are open to the research community to facilitate the assessment of the automated driving functions in different scenarios.Comment: 6 pages, accepted by ITSC 202

PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation

Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk. As a result, they are only applicable in scenarios where those assumptions hold. To address this issue, we present a novel data-driven Probabilistic RISk Measure derivAtion (PRISMA) method. The PRISMA method is used to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). Because we adopt a data-driven approach to predict the possible future evolutions of traffic participant trajectories, less assumptions on these trajectories are needed. Since the PRISMA is not bound to specific assumptions, multiple SSMs for different types of scenarios can be derived. To calculate the probability of the specific event, the PRISMA method uses Monte Carlo simulations to estimate the occurrence probability of the specified event. We further introduce a statistical method that requires fewer simulations to estimate this probability. Combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. It is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.Comment: 26 pages, 4 figures, 1 tabl

Identification of patients at risk of sudden cardiac death in congenital heart disease:The PRospEctiVE study on implaNTable cardlOverter defibrillator therapy and suddeN cardiac death in Adults with Congenital Heart Disease (PREVENTION-ACHD)

BACKGROUND Sudden cardiac death (SCD) is the main preventable cause of death in patients with adult congenital heart disease (ACHD). Since robust risk stratification methods are lacking, we developed a risk score model to predict SCD in patients with ACHD: the PRospEctiVE study on implaNTable cardlOverter defibrillator therapy and suddeN cardiac death in Adults with Congenital Heart Disease (PREVENTION-ACHD) risk score model. OBJECTIVE The purpose of this study was to prospectively study predicted SCD risk using the PREVENTION-ACHD risk score model and actual SCD and sustained ventricular tachycardia/ventricular fibrillation (VT/VF) rates in patients with ACHD. METHODS The PREVENTION-ACHD risk score model assigns 1 point each to coronary artery disease, New York Heart Association class II/III heart failure, supraventricular tachycardia, systemic ejection fraction = 120 ms, and QT dispersion >= 70 ms. SCD risk was calculated for each patient. An annual predicted risk of >= 3% constituted high risk. The primary outcome was SCD or VT/VF after 2 years. The secondary outcome was SCD. RESULTS The study included 783 consecutive patients with ACHD (n=239 (31%) left-sided lesions; n=138 (18%) tetralogy of Fallot; n=108 (14%) dosed atrial septal defect; median age 36 years; interquartile range 28-47 years; n=401 (51%) men). The PREVENTION-ACHD risk score modelidentified 58 high-risk patients. Eight patients (4 at high risk) experienced the primary outcome. The Kaplan-Meier estimates were 7% (95% confidence interval [CI] 0.1%-13.3%) in the high-risk group and 0.6% (95% CI 0.0%-1.1%) in the low-risk group (hazard ratio 12.5; 95% CI 3.1-50.9; P < .001). The risk score model's sensitivity was 0.5 and specificity 93, resulting in a C-statistic of 0.75 (95% CI 0.57-0.90). The hazard ratio for SCD was 12.4 (95% CI 1.8-88.1) (P = .01); the sensitivity and specificity were 0.5 and 0.92, and the C-statistic was 0.81 (95% CI 0.67-0.95). CONCLUSION The PREVENTION-ACHD risk score model provides greater accuracy in SCD or VT/VF risk stratification as compared with current guideline indications and identifies patients with ACHD who may benefit from preventive implantable cardioverterdefibrillator implantation

Cut-in scenario prediction for automated vehicles

Truck platooning is gaining more and more interest thanks to the benefits on improved traffic efficiency, reduced fuel consumption and emissions. To gain these benefits, it typically involves small following distances (0.8 s - 0.3 s). Due to the small following distances, the cut-in manoeuvre of target vehicles becomes safety critical and requires the platooning system to take action as soon as possible. This work shows how machine learning can be used for the prediction of a cut-in manoeuvre of a vehicle, which we refer to as target vehicle, from a host vehicle perspective. A real-life driving experiment was performed to measure several cut-ins that were manually annotated. Measurements are gathered with a lidar installed on the host vehicle and consequently used to train several well-known machine learning algorithms such as Logistic Regression, Random Forest, Support Vector Machine, Adaboost and an Ensemble of the previous models. The Ensemble model achieves the best results. This method is capable of predicting cut-ins prior to their occurrence, with an f 1 score of 62.28 % on the test set. Moreover, over 60% of the cut-ins are correctly predicted more than one second before the corresponding vehicle crosses the lane marker

Cut-in scenario prediction for automated vehicles

Truck platooning is gaining more and more interest thanks to the benefits on improved traffic efficiency, reduced fuel consumption and emissions. To gain these benefits, it typically involves small following distances (0.8 s - 0.3 s). Due to the small following distances, the cut-in manoeuvre of target vehicles becomes safety critical and requires the platooning system to take action as soon as possible. This work shows how machine learning can be used for the prediction of a cut-in manoeuvre of a vehicle, which we refer to as target vehicle, from a host vehicle perspective. A real-life driving experiment was performed to measure several cut-ins that were manually annotated. Measurements are gathered with a lidar installed on the host vehicle and consequently used to train several well-known machine learning algorithms such as Logistic Regression, Random Forest, Support Vector Machine, Adaboost and an Ensemble of the previous models. The Ensemble model achieves the best results. This method is capable of predicting cut-ins prior to their occurrence, with an f 1 score of 62.28 % on the test set. Moreover, over 60% of the cut-ins are correctly predicted more than one second before the corresponding vehicle crosses the lane marker

Application of AdaptIVe Evaluation Methodology.

Since the last decade, development efforts by academia and industry for automated driving functions have increased significantly. Also, the European research project AdaptIVe is looking into this topic. Within AdaptIVe, 21 different automated driving functions for different speed ranges and target areas have been developed. They have been developed in three sub projects (SPs), addressing different automation scenarios:Sub project 4: Automation in close-distance scenarios: Addresses manoeuvres at low speed (below 30 km/h) that are characterised by the presence of close obstacles, such as in parking manoeuvres.Sub project 5: Automation in urban scenarios: Deals with driving scenarios in urban environments that are characterised by an average speed range of 0 to 70 km/h.Sub project 6: Automation in highway scenarios: Addresses motorway scenarios (or motorway similar roads) considering velocities up to 130 km/h. Due to the large operation spaces and various complex situations that are covered by these functions, efforts for evaluation are expected to increase significantly. In order to enable an efficient assessment of automated driving functions, within the subproject 7 a comprehensive evaluation methodology addressing this challenge has been developed. Technical Assessment: Evaluates the performance of the developed automated driving functions with respect to a defined baseline.User-related Assessment: Analyses the interaction between the function and the user, trust, usability as well as acceptance of the developed functions.In-Traffic Assessment: Focuses on the effects of the surrounding traffic on the automated driving function as well as the effects of the automated driving function on the surrounding non-users.Impact Assessment: Determines the potential effects of the function with respect to safety and environmental aspects (e.g. fuel consumption, traffic efficiency).The evaluation methodologies developed in previous research projects dealt mainly with active safety functions, for which the assessment focused mainly on testing of functions’ use cases. For automated driving the assessment approach needs to be extended in order to ensure that the whole situation space which is addressed by the functions is covered. Therefore, in the developed evaluation approach the test resources are allocated based on the functions’ classification in order to enable a holistic and efficient assessment. Hence, the automated driving functions are classified based on their automation level and their operation time in two different function types: •Functions that operate only for a short period of time (seconds up to few minutes). Typical examples are automated parking functions and the minimum risk manoeuvre function. These functions are called "event based”.•Functions that, once they are active, can operate over a longer period of time (minutes up to hours). A typical example of this type of function is a “highway pilot”. They are called "continuously operating" functions