arXiv subjects

Jonas Bärgman

Publications and source records attributed to Jonas Bärgman.

15 recordsLinked to original sources

arXiv2026

Practical validation of synthetic pre-crash scenarios

The representativeness of synthetic pre-crash scenarios is crucial for assessing the safety impact of Driving Automation Systems through virtual simulations. However, a gap remains in the robust evaluation of synthetic pre-crash scenarios' practical equivalence to their real-world counterparts; that is, whether they are similar enough for the intended assessment purpose. Conventional significance testing is inadequate, as it focuses on detecting differences rather than establishing practical equivalence. This study addresses the research gap by extending our previous work on a Bayesian Region of Practical Equivalence (ROPE)-based equivalence testing framework by introducing a binning-based approach to define appropriate statistics and equivalence criteria. Two binning-based statistics are proposed to measure practically meaningful distributional differences between datasets in the context of safety impact assessment. The framework's applicability is demonstrated through a case study, which tests the practical equivalence of two synthetic rear-end pre-crash datasets with a previously developed reference dataset in the context of the safety impact assessment of an Automatic Emergency Braking system. The results show that the framework provides informative quantitative assessments of practical equivalence as well as diagnostic insights into the divergence of datasets. Although the demonstration focuses on rear-end pre-crash scenarios, the framework is generic and extensible to broader validation contexts, providing an interpretable and principled basis for practical equivalence assessment across diverse synthetic data applications.

cs.RO↗

arXiv2025

Practical Equivalence Testing and Its Application in Synthetic Pre-Crash Scenario Validation

The use of representative pre-crash scenarios is critical for assessing the safety impact of driving automation systems through simulation. However, a gap remains in the robust evaluation of the similarity between synthetic and real-world pre-crash scenarios and their crash characteristics. Without proper validation, it cannot be ensured that the synthetic test scenarios adequately represent real-world driving behaviors and crash characteristics. One reason for this validation gap is the lack of focus on methods to confirm that the synthetic test scenarios are practically equivalent to real-world ones, given the assessment scope. Traditional statistical methods, like significance testing, focus on detecting differences rather than establishing equivalence; since failure to detect a difference does not imply equivalence, they are of limited applicability for validating synthetic pre-crash scenarios and crash characteristics. This study addresses this gap by proposing an equivalence testing method based on the Bayesian Region of Practical Equivalence (ROPE) framework. This method is designed to assess the practical equivalence of scenario characteristics that are most relevant for the intended assessment, making it particularly appropriate for the domain of virtual safety assessments. We first review existing equivalence testing methods. Then we propose and demonstrate the Bayesian ROPE-based method by testing the equivalence of two rear-end pre-crash datasets. Our approach focuses on the most relevant scenario characteristics. Our analysis provides insights into the practicalities and effectiveness of equivalence testing in synthetic test scenario validation and demonstrates the importance of testing for improving the credibility of synthetic data for automated vehicle safety assessment, as well as the credibility of subsequent safety impact assessments.

cs.RO↗

arXiv2025

Evaluation of adaptive sampling methods in scenario generation for virtual safety impact assessment of pre-crash safety systems

Virtual safety assessment plays a vital role in evaluating the safety impact of pre-crash safety systems such as advanced driver assistance systems (ADAS) and automated driving systems (ADS). However, as the number of parameters in simulation-based scenario generation increases, the number of crash scenarios to simulate grows exponentially, making complete enumeration computationally infeasible. Efficient sampling methods, such as importance sampling and active sampling, have been proposed to address this challenge. However, a comprehensive evaluation of how domain knowledge, stratification, and batch sampling affect their efficiency remains limited. This study evaluates the performance of importance sampling and active sampling in scenario generation, incorporating two domain-knowledge-driven features: adaptive sample space reduction (ASSR) and stratification. Additionally, we assess the effects of a third feature, batch sampling, on computational efficiency in terms of both CPU and wall-clock time. Based on our findings, we provide practical recommendations for applying ASSR, stratification, and batch sampling to optimize sampling performance. Our results demonstrate that ASSR substantially improves sampling efficiency for both importance sampling and active sampling. When integrated into active sampling, ASSR reduces the root mean squared estimation error (RMSE) of the estimates by up to 90\%. Stratification further improves sampling performance for both methods, regardless of ASSR implementation. When ASSR and/or stratification are applied, importance sampling performs on par with active sampling, whereas when neither feature is used, active sampling is more efficient. Larger batch sizes reduce wall-clock time but increase the number of simulations required to achieve the same estimation accuracy.

stat.AP↗

arXiv2025

Strategic decision points in experiments: A predictive Bayesian optional stopping method

Sample size determination is crucial in experimental design, especially in traffic and transport research. Frequentist statistics require a fixed sample size determined by power analysis, which cannot be adjusted once the experiment starts. Bayesian sample size determination, with proper priors, offers an alternative. Bayesian optional stopping (BOS) allows experiments to stop when statistical targets are met. We introduce predictive Bayesian optional stopping (pBOS), combining BOS with Bayesian rehearsal simulations to predict future data and stop experiments if targets are unlikely to be met within resource constraints. We identified and corrected a bias in predictions using multiple linear regression. pBOS shows up to 118% better cost benefit than traditional BOS and is more efficient than frequentist methods. pBOS allows researchers to, under certain conditions, stop experiments when resources are insufficient or when enough data is collected, optimizing resource use and cost savings.

stat.ME↗

arXiv2024

RAVE Checklist: Recommendations for Overcoming Challenges in Retrospective Safety Studies of Automated Driving Systems

The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion of ADS technology deployments is paving the way for early stage safety impact evaluations, whereby the observational data from both an ADS and a representative benchmark fleet are compared to quantify safety performance. In January 2024, a working group of experts across academia, insurance, and industry came together in Washington, DC to discuss the current and future challenges in performing such evaluations. A subset of this working group then met, virtually, on multiple occasions to produce this paper. This paper presents the RAVE (Retrospective Automated Vehicle Evaluation) checklist, a set of fifteen recommendations for performing and evaluating retrospective ADS performance comparisons. The recommendations are centered around the concepts of (1) quality and validity, (2) transparency, and (3) interpretation. Over time, it is anticipated there will be a large and varied body of work evaluating the observed performance of these ADS fleets. Establishing and promoting good scientific practices benefits the work of stakeholders, many of whom may not be subject matter experts. This working group's intentions are to: i) strengthen individual research studies and ii) make the at-large community more informed on how to evaluate this collective body of work.

cs.RO↗

arXiv2024

Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods.

stat.ME↗

arXiv2024

Model-based generation of representative rear-end crash scenarios across the full severity range using pre-crash data

Generating representative rear-end crash scenarios is crucial for safety assessments of Advanced Driver Assistance Systems (ADAS) and Automated Driving systems (ADS). However, existing methods for scenario generation face challenges such as limited and biased in-depth crash data and difficulties in validation. This study sought to overcome these challenges by combining naturalistic driving data and pre-crash kinematics data from rear-end crashes. The combined dataset was weighted to create a representative dataset of rear-end crash characteristics across the full severity range in the United States. Multivariate distribution models were built for the combined dataset, and a driver behavior model for the following vehicle was created by combining two existing models. Simulations were conducted to generate a set of synthetic rear-end crash scenarios, which were then weighted to create a representative synthetic rear-end crash dataset. Finally, the synthetic dataset was validated by comparing the distributions of parameters and the outcomes (Delta-v, the total change in vehicle velocity over the duration of the crash event) of the generated crashes with those in the original combined dataset. The synthetic crash dataset can be used for the safety assessments of ADAS and ADS and as a benchmark when evaluating the representativeness of scenarios generated through other methods.

cs.RO↗

arXiv2024

Validation of human benchmark models for Automated Driving System approval: How competent and careful are they really?

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are expected to improve comfort, productivity and, most importantly, safety for all road users. To ensure that the systems are safe, rules and regulations describing the systems' approval and validation procedures are in effect in Europe. The UNECE Regulation 157 (R157) is one of those. Annex 3 of R157 describes two driver models, representing the performance of a "competent and careful" driver, which can be used as benchmarks to determine whether, in certain situations, a crash would be preventable by a human driver. However, these models have not been validated against human behavior in real safety-critical events. Therefore, this study uses counterfactual simulation to assess the performance of the two models when applied to 38 safety-critical cut-in near-crashes from the SHRP2 naturalistic driving study. The results show that the two computational models performed rather differently from the human drivers: one model showed a generally delayed braking reaction compared to the human drivers, causing crashes in three of the original near-crashes. The other model demonstrated, in general, brake onsets substantially earlier than the human drivers, possibly being overly sensitive to lateral perturbations. That is, the first model does not seem to behave as the competent and careful driver it is supposed to represent, while the second seems to be overly careful. Overall, our results show that, if models are to be included in regulations, they need to be substantially improved. We argue that achieving this will require better validation across the scenario types that the models are intended to cover (e.g., cut-in conflicts), a process which should include applying the models counterfactually to near-crashes and validating them against several different safety related metrics.

eess.SY↗

arXiv2024

Requirements Strategy for Managing Human Factors in Automated Vehicle Development

The integration of human factors (HF) knowledge is crucial when developing safety-critical systems, such as automated vehicles (AVs). Ensuring that HF knowledge is considered continuously throughout the AV development process is essential for several reasons, including efficacy, safety, and acceptance of these advanced systems. However, it is challenging to include HF as requirements in agile development. Recently, Requirements Strategies have been suggested to address requirements engineering challenges in agile development. By applying the concept of Requirements Strategies as a lens to the investigation of HF requirements in agile development of AVs, this paper arrives at three areas for investigation: a) ownership and responsibility for HF requirements, b) structure of HF requirements and information models, and c) definition of work and feature flows related to HF requirements. Based on 13 semi-structured interviews with professionals from the global automotive industry, we provide qualitative insights in these three areas. The diverse perspectives and experiences shared by the interviewees provide insightful views and helped to reason about the potential solution spaces in each area for integrating HF within the industry, highlighting the real-world practices and strategies used.

cs.SE↗

arXiv2024

Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices

Due to the technical complexity and social impact, automated vehicle (AV) development challenges the current state of automotive engineering practice. Research shows that it is important to consider human factors (HF) knowledge when developing AVs to make them safe and accepted. This study explores the current practices and challenges of the automotive industries for incorporating HF requirements during agile AV development. We interviewed ten industry professionals from several Swedish automotive companies, including HF experts and AV engineers. Based on our qualitative analysis of the semi-structured interviews, a number of current approaches for communicating and incorporating HF knowledge into agile AV development and associated challenges are discussed. Our findings may help to focus future research on issues that are critical to effectively incorporate HF knowledge into agile AV development.

cs.SE↗

arXiv2024

Methodological challenges of scenario generation validation: a rear-end crash-causation model for virtual safety assessment

Safety assessment of crash and conflict avoidance systems is important for both the automotive industry and other stakeholders. One type of system that needs such an assessment is a driver monitoring system (DMS) with some intervention (e.g., warning or nudging) when the driver looks off-road for too long. Although using computer simulation to assess safety systems is becoming increasingly common, it is not yet commonly used for systems that affect driver behavior, such as DMSs. Models that generate virtual crashes, taking crash-causation mechanisms into account, are needed to assess these systems. However, few such models exist, and those that do have not been thoroughly validated on real-world data. This study aims to address this research gap by validating a rear-end crash-causation model which is based on four crash-causation mechanisms related to driver behavior: a) off-road glances, b) too-short headway, c) not braking with the maximum deceleration possible, and d) sleepiness (not reacting before the crash). The pre-crash kinematics were obtained from the German GIDAS in-depth crash database. Challenges with the validation process were identified and addressed. Most notably, a process was developed to transform the generated crashes to mimic the crash severity distribution in GIDAS. This step was necessary because GIDAS does not include property-damage-only (PDO) crashes, while the generated crashes cover the full range of severities (including low-severity crashes, of which many are PDOs). Our results indicate that the proposed model is a reasonably good crash generator. We further demonstrated that the model is a valid method for assessing DMSs in virtual simulations; it shows the safety impact of shorter "longest" off-road glances. As expected, cutting away long off-road glances substantially reduces the number of crashes that occur and reduces the average delta-v.

stat.AP↗

arXiv2023

Continuous Experimentation and Human Factors An Exploratory Study

In todays rapidly evolving technological landscape, the success of tools and systems relies heavily on their ability to meet the needs and expectations of users. User-centered design approaches, with a focus on human factors, have gained increasing attention as they prioritize the human element in the development process. With the increasing complexity of software-based systems, companies are adopting agile development methodologies and emphasizing continuous software experimentation. However, there is limited knowledge on how to effectively execute continuous experimentation with respect to human factors within this context. This research paper presents an exploratory qualitative study for integrating human factors in continuous experimentation, aiming to uncover distinctive characteristics of human factors and continuous software experiments, practical challenges for integrating human factors in continuous software experiments, and best practices associated with the management of continuous human factors experimentation.

cs.SE↗

arXiv2023

Modeling Lead-vehicle Kinematics For Rear-end Crash Scenario Generation

The use of virtual safety assessment as the primary method for evaluating vehicle safety technologies has emphasized the importance of crash scenario generation. One of the most common crash types is the rear-end crash, which involves a lead vehicle and a following vehicle. Most studies have focused on the following vehicle, assuming that the lead vehicle maintains a constant acceleration/deceleration before the crash. However, there is no evidence for this premise in the literature. This study aims to address this knowledge gap by thoroughly analyzing and modeling the lead vehicle's behavior as a first step in generating rear-end crash scenarios. Accordingly, the study employed a piecewise linear model to parameterize the speed profiles of lead vehicles, utilizing two rear-end pre-crash/near-crash datasets. These datasets were merged and categorized into multiple sub-datasets; for each one, a multivariate distribution was constructed to represent the corresponding parameters. Subsequently, a synthetic dataset was generated using these distribution models and validated by comparison with the original combined dataset. The results highlight diverse lead-vehicle speed patterns, indicating that a more accurate model, such as the proposed piecewise linear model, is required instead of the conventional constant acceleration/deceleration model. Crashes generated with the proposed models accurately match crash data across the full severity range, surpassing existing lead-vehicle kinematics models in both severity range and accuracy. By providing more realistic speed profiles for the lead vehicle, the model developed in the study contributes to creating realistic rear-end crash scenarios and reconstructing real-life crashes.

cs.RO↗

arXiv2023

Exploring Turn Signal Usage Patterns in Lane Changes: A Bayesian Hierarchical Modelling Analysis of Realistic Driving Data

Using turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals prior to starting a lane change. In this study, we analyze realistic driving data to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. We examine turn signal usage and identify factors that have an influence on it by employing Bayesian hierarchical modelling (BHM). The results showed that a turn signal was used in approximately 60% of cases before starting a lane change, while it was only used after the start of a lane change in 33% of cases. In 7% of cases, a turn signal was not used at all. Additionally, the BHM results reveal that various factors influence turn signal usage. The study concludes that understanding the factors that affect turn signal usage is crucial for improving traffic safety through policy-making and designing algorithms for autonomous vehicles for future mixed traffic.

stat.AP↗

arXiv2023

Critical Zones for Comfortable Collision Avoidance with a Leading Vehicle

This paper provides a general framework for efficiently obtaining the appropriate intervention time for collision avoidance systems to just avoid a rear-end crash. The proposed framework incorporates a driver comfort model and a vehicle model. We show that there is a relationship between driver steering manoeuvres based on acceleration and jerk, and steering angle and steering angle rate profiles. We investigate how four different vehicle models influence the time when steering needs to be initiated to avoid a rear-end collision. The models assessed were: a dynamic bicycle model (DM), a steady-state cornering model (SSCM), a kinematic model (KM) and a point mass model (PMM). We show that all models can be described by a parameter-varying linear system. We provide three algorithms for steering that use a linear system to compute the intervention time efficiently for all four vehicle models. Two of the algorithms use backward reachability simulation and one uses forward simulation. Results show that the SSCM, KM and PMM do not accurately estimate the intervention time for a certain set of vehicle conditions. Due to its fast computation time, DM with a backward reachability algorithm can be used for rapid offline safety benefit assessment, while DM with a forward simulation algorithm is better suited for online real-time usage.

cs.RO↗