Safety Of Automated Vehicles Research Articles

REVOLUTIONIZING MOBILITY: A COMPREHENSIVE EVALUATION OF THE IMPACT OF AI ON AUTOMATED VEHICLES

Automated vehicles (AVs) represent a game-changing advancement in transportation, utilizing cutting-edge technologies to travel without human intervention. These vehicles, which range from advanced driver assistance systems (ADAS) to completely autonomous versions, rely on artificial intelligence (AI) to perceive and make decisions. Recent advances in sensor technology and machine learning have improved AV capabilities, but understanding how AI responds to various distance metrics is critical for assuring safe journeys. Hypothesizing that the more the distance the car is traveling, the more AI may be able to provide a safe journey for the passengers,this study seeks to investigate the complex relationship between distance and AIs ability to ensure AV safety. Investigating the physical, geographical, and temporal characteristics of distance can help optimize AV performance and reduce safety issues.

Analysis of a Hybrid Model of Automated Vehicle Safety System Consists of Rain Sensor-Controlled Wiper and Ultrasonic Sensor-Based Auto-Braking System

This paper presents an analysis of a novel hybrid model integrating two crucial safety features in modern vehicles: a Rain Sensor-Controlled Wiper (RSCW) and an Ultrasonic Sensor-Based Auto-Braking System (US-ABS) using Arduino UNO micro-controller. The integration of these systems aims to enhance vehicle safety by addressing two critical aspects: visibility during adverse weather conditions and collision avoidance through automated braking mechanisms. The Rain Sensor-Controlled Wiper (RSCW) component utilizes advanced sensor technology to detect precipitation on the vehicle's windshield accurately. Upon detection, the system automatically activates the wiper mechanism, tuning the wiper speed and frequency according to the amount of rainfall. By ensuring optimal visibility, the RSCW system significantly reduces the possibility of mishaps brought on by vision impairment due to rain, snow, or other weather phenomena. Complementing the RSCW, the Ultrasonic Sensor-Based Auto-Braking System (US-ABS) serves as a proactive safety measure to prevent collisions. Employing ultrasonic sensors strategically positioned around the vehicle, the US-ABS continuously monitors the surrounding environment for obstacles, vehicles, or pedestrians. When an imminent collision risk is detected, the system triggers automatic braking interventions to mitigate or entirely prevent potential accidents. In this analysis, we evaluate the effectiveness of the hybrid model by considering various factors, including accuracy, response time, and real-world performance. We have explored potential synergies between the RSCW and US-ABS components, assessing how their integration can further improve overall safety and reliability.

Freeway merging trajectory prediction for automated vehicles using naturalistic driving data

Due to differences in speed and the complex interaction between merging and through-lane vehicles at freeway merging sections, crashes involving both human drivers and automated vehicles (AVs) persist. To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning, researchers have focused on developing trajectory prediction algorithms. Few studies, however, have developed merging trajectory prediction models using naturalistic driving data in China, making it urgent to put it on the agenda for AVs’ safety and efficiency at freeway merging sections. Based on merging periods extracted from the Shanghai Naturalistic Driving Study (SH-NDS), this study compares merging behavior on freeways with through-lane speed limits of 80 km/h, 100 km/h, and 120 km/h using analysis of variance (ANOVA). Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network (BPNN) and long short-term memory neural network (LSTM NN) approaches. Results show 1) significant differences among the three cases in all merging behavior variables except for longitudinal gap; and that 2) the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTM NN. Two major contributions to the safe operation of AVs are provided: 1) the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions, and assist ongoing motion planning strategies for AVs; and 2) the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.

The typical AV accident scenarios in the urban area obtained by clustering and association rule mining of real-world accident reports

Automated Vehicles (AVs) based on a collection of advanced technologies such as big data and artificial intelligence have opened an opportunity to reduce traffic accidents caused by human drivers. Nevertheless, traffic accidents of AVs continue to occur, which raises safety and reliability concerns about AVs. AVs are particularly vulnerable to accidents on urban roads than on highways due to various dynamic objects and more complex infrastructure. Several studies proposed a scenario-based approach of experimenting with the response of AVs to specific situations as a way to test their safety. Reliable and concrete scenarios are necessary to test AV safety under critical conditions accurately. This study aims to derive a typical accident scenario for evaluating the safety of AVs, specifically in urban areas, by analysing collisions reported by the DMV of California, USA. We applied a hierarchical clustering method to find groups of similar reports and then executed association rule mining on each cluster to correlate between accident factors and collision types. We combined statistically significant association rules to constitute a total of 14 scenarios that are described according to an adapted PEGASUS framework. The newly obtained scenarios exhibit significantly different accident patterns than the typical Human-driven Vehicles (HVs) in urban areas reported by National Highway Traffic Safety Administration. Our discovery urges AV safety to be tested reliably under scenarios more relevant than the existing HV accident scenarios.

Evidence of automated vehicle safety’s influence on people’s acceptance of the automated driving technology

Existing studies identified targeted audiences showing increases in Automated Vehicles (AV) acceptance after experiencing automated driving. However, there is still uncertainty regarding the reasons. Although some studies cited safety as the primary reason, there is no objective evidence from safety performance in verifying its impact on AV acceptance. This study contributes to the literature by quantitatively revealing why AV acceptance is changed after experiencing automated driving via a Structural Equation Modeling (SEM) method and objectively validating that safety is the primary factor in determining AV acceptance. Sixty drivers completed driving tasks on a driving simulator under Levels 0, 4, 3, and 2 and survey questions in between. As a result, the safety-related perceptions of AV were identified as reasons for affecting AV acceptance. Particularly, the evaluation of traffic conflicts and conflict severity validates the results from SEM, proving that safety is the primary and significant reason for influencing AV acceptance.

Is trust a belief, observation, or state: Results from an active inference analysis of driver-automation transitions of control

Transitions of control are an important safety concern for human-automation teams and automated vehicle safety. While trust and situation awareness have been observed to influence transitions of control in automated vehicles, there are few objective measurements, making these concepts difficult to operationalize in increasingly automated decision systems. In this study, we take a step towards quantifying trust by mapping latent driver beliefs extracted from an active inference-factor analysis model of driver behavior and cognitive dynamics to subjective responses to trust questionnaires. Our results show that subjective trust is primarily correlated with model parameters affecting perceptual evidence accumulation rate, and the same parameters are significantly correlated with driver age.

Understanding non-motorists' views on automated vehicle safety through Bayesian network analysis and latent dirichlet allocation

Automated vehicles (AVs) hold great promise for creating a safer, more efficient, more equitable, and more sustainable transportation system. However, the rapid adoption of AVs requires a thorough understanding in their coexistence with the human environment in the current roadway network, particularly with respect to interactions between AVs and non-motorists. Bike Pittsburgh (BikePGH) conducted a 2019 survey to examine non-motorists' perceptions of AV safety. Using Bayesian network (BN) analysis, the study identified key factors such as safety perception, AV technology knowledge, and real-world interaction experiences that influence non-motorists' overall perception of AV safety using BikePGH survey data. The study also explored several counterfactual scenarios to gain insights into non-motorists' viewpoints on AV safety. Notably, the study found that the differences in the ways of AVs and human-driven vehicles interacted with non-motorists at intersections played a crucial role in shaping survey participants' opinions. By taking into account the key insights identified in this study, policymakers can develop evidence-based strategies to achieve sustainable urban mobility goals while ensuring the safety and well-being of all road users, particularly non-motorists.

A Finite-Sampling, Operational Domain Specific, and Provably Unbiased Connected and Automated Vehicle Safety Metric

A connected and automated vehicle safety metric determines the performance of a subject vehicle (SV) by analyzing the data involving the interactions among the SV and other dynamic road users and environmental features. When the data set contains only a finite set of samples collected from the naturalistic mixed-traffic driving environment, a metric is expected to generalize the safety assessment outcome from the observed finite samples to the unobserved cases by specifying in what domain the SV is expected to be safe and how safe the SV is, statistically, in that domain. However, to the best of our knowledge, none of the existing safety metrics are able to justify the above properties with an operational domain specific, guaranteed complete, and provably unbiased safety evaluation outcome. In this paper, we propose a novel safety metric that involves the $\alpha$-shape and the $\epsilon$-almost robustly forward invariant set to characterize the SV's almost safe operable domain and the probability for the SV to remain inside the safe domain indefinitely, respectively. The empirical performance of the proposed method is demonstrated in several different operational design domains through a series of cases covering a variety of fidelity levels (real-world and simulators), driving environments (highway, urban, and intersections), road users (car, truck, and pedestrian), and SV driving behaviors (human driver and self driving algorithms).

Normalizing crash risk of partially automated vehicles under sparse data

The safety of increasingly automated vehicles is of great concern to regulators, yet crash rates are generally reported by manufacturers using proprietary metrics with limited source data. Without consistent definitions of crashes and exposure, automated vehicle crash rates cannot be meaningfully compared with baseline datasets. The objective of this study was to establish methods to normalize automated vehicle crash rates using one manufacturer’s crash reports as a case study. The manufacturer’s quarterly crash rates for vehicles using SAE Level 1 and Level 2 automation were compared. Road type was controlled for using data from a naturalistic driving study with the same model vehicles, while driver age was controlled for using demographic ownership surveys. Although Level 2 vehicles were claimed to have a 43% lower crash rate than Level 1 vehicles, their improvement was only 10% after controlling for different rates of freeway driving. Direct comparison with general public driving was impossible due to unclear crash severity thresholds in the manufacturer’s reports, but analysis showed that controlling for driver age would increase reported crash rates by 11%. These results establish the need for detailed crash data, crash definitions, and exposure and demographic data in order to accurately assess automated vehicle safety.

A Reasonable Driver Standard for Automated Vehicle Safety

A Reasonable Driver Standard for Automated Vehicle Safety

Formal Certification Methods for Automated Vehicle Safety Assessment

Challenges related to automated driving are no longer focused on just the construction of such automated vehicles (AVs), but in assuring the safety of their operation. Recent advances in Level 3 and Level 4 autonomous driving have motivated more extensive study in safety guarantees of complicated AV maneuvers, which aligns with the goal of ISO 21448 (Safety of the Intended Functions, or SOTIF), i.e. minimizing unsafe scenarios both known and unknown, as well as Vision Zero -- eliminating highway fatalities by 2050. A majority of approaches used in providing safety guarantees for AV motion control originate from formal methods, especially reachability analysis (RA), which relies on mathematical models for the dynamic evolution of the system to provide guarantees. However, to the best of the authors' knowledge, there have been no review papers dedicated to describing and interpreting state-of-the-art of formal methods in the context of AVs. In this work, we provide both an overview of the safety verification, validation and certification process, as well as review formal safety techniques that are best suited to AV applications. We also propose a unified scenario coverage framework that can provide either a formal or sample-based estimate of safety verification for full AVs. Finally, remaining challenges and future opportunities beyond the scope of current published research for assured AV safety are presented.

Breaking the Tyranny of Net Risk Metrics for Automated Vehicle Safety

Breaking the Tyranny of Net Risk Metrics for Automated Vehicle Safety

Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis

Vehicle automation, manifested in self-driving cars, promises to provide safe mobility by reducing human errors. While the testing of automated vehicles (AVs) has improved their performance in recent years, automation technologies face challenges such as uncertainty of safety impacts in mixed traffic with human-driven vehicles. This study aims to examine the gaps in AV safety performance and identify what will be required on a preferential basis for AVs to guarantee an acceptable level of safety performance, especially in mixed traffic, by conducting a thorough analysis of crashes involving levels 2–3 AVs. Based on 260 AV collision reports from California from 2019 to 2021, this study extracts crash-related variables from crash records in a standardized form, crash locations, and, notably, crash narratives reported by AV manufacturers. This study untangles the complex interrelationships among pre-crash conditions, AV driving modes, crash types, and crash outcomes by applying a path-analytic framework with the frequentist and Bayesian approaches. Results show that 51.9 percent of crashes were rear-ends. Particularly, AVs become more vulnerable to rear-end collisions in the automated driving mode than in the conventional mode, given a crash. Additionally, the automated driving mode would not significantly affect the chance of a sideswipe collision, injury, or AV damage levels. Another interesting finding is that manual disengagement is more likely to happen when an AV interacts with a transit vehicle right before a crash occurs while having a negative relationship with injury crashes. Moreover, to reduce injury crashes, AVs would need more thorough testing to adapt to the critical roadway and infrastructure features such as intersections, ramps, and slip lanes; and roadway infrastructure would require improvements to support transportation automation. The risk factors identified in this study can be considered in AV safety assessment scenarios and future operations of mixed traffic. This study demonstrates that AV crash narrative data can be leveraged to improve knowledge of AV safety in mixed traffic.

Vehicle crash mitigation strategy in unavoidable collision scenarios: focusing on motion planning by considering a generalized crash severity model

Driving strategy in dynamic environment is crucial to the automated vehicle safety. In extremely emergency scenarios with unavoidable collision (UC), especially those with complex impact patterns, the potential crash risk should be well considered. This paper proposes a crash mitigation (CM) algorithm for UCs, which directly embeds a generalized crash severity index (CSI) model to vehicle-to-vehicle collisions of multiple impact patterns. The idea is that during the short time before a collision, the vehicle will actively adapt its position and poses to minimize the potential crash severity level after the collision. To this end, the generalized CSI model is introduced to estimate the potential crash severity of all sample paths, from which a crash-severity-optimal trajectory is obtained. To improve the inferring time efficiency of the planning module, a neural network is constructed and deployed to approximate the nonlinear severity model. The proposed algorithm is first validated through simulations of UC scenarios, including entry ramp merging, intersection crossing and downhill/uphill crossing. Then for the intersection crossing scenario, the algorithm is deployed to a real car and validated through digital-twin experiments. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly used collision avoidance (CA) strategies. This reveals that a new mindset of comprehensive safety strategy should not focus only on CA, but also the last resort of CM if collision is unavoidable. Our work may contribute as a promising solution to the safety problem in emergency scenarios.

A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences

Introduction: Automated vehicle (AV) technology is a promising technology for improving the efficiency of traffic operations and reducing emissions. This technology has the potential to eliminate human error and significantly improve highway safety. However, little is known about AV safety issues due to limited crash data and relatively fewer AVs on the roadways. This study provides a comparative analysis between AVs and conventional vehicles on the factors leading to different types of collisions. Method: A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective. Four years (2017–2020) of AV and conventional vehicle crash data on California roads were used. The AV crash dataset was acquired from the California Department of Motor Vehicles, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database. A buffer of 50 feet was used to associate each AV crash and conventional vehicle crash; a total of 127 AV crashes and 865 conventional vehicle crashes were used for analysis. Results: Our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes. Further, AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.), respectively, when compared to conventional vehicles. The variables associated with the increased likelihood of rear-end collisions for AVs include signalized intersections and lanes with less than 45 mph speed limit. Conclusions: Although AVs are found to improve safety on the road in most types of collisions by limiting human error leading to vehicle crashes, the current state of the technology shows that safety aspects still need improvement.

Exploration on prior driving modes for automated vehicle collisions

ABSTRACT The emergence of automated vehicles (AV) has been occurring rapidly as these vehicles have the potential to reduce/eradicate human driving faults and related collisions. To enhance AV safety, the NHTSA recommends the continuous presence of a backup human driver that has a reasonable understanding of AV technologies to ensure disengagement when manual overtake is required. However, due to several AV-related traffic crashes during roadway testing and extensive media interest, AV safety has become a critical issue. This study collected 255 crash reports filed by different manufacturers testing AVs in California from September 2014 to April 2020. The crash dataset was analyzed using two data mining algorithms (association rule mining and text network analysis) to identify the key AV-related crash attributes and their associations based on the vehicle’s prior driving mode (conventional or automated). The results show that the manner of collision and the prior movement of the testing vehicle are strongly connected with prior driving mode. For example, AV crashes in a manual driving mode often result in a sideswipe collision during moving status, whereas AV crashes in an automated driving mode are highly associated with rear-end collisions when AVs are stopped in traffic. The findings of this study can help policymakers and AV engineers improve AV deployment strategies to support the adoption of AVs and promote potential safety benefits for AV technologies.

Suggestion of Evaluation Elements Based on ODD for Automated Vehicles Safety Verification : Case of K-City

Suggestion of Evaluation Elements Based on ODD for Automated Vehicles Safety Verification : Case of K-City

Adaptive Design of Experiments for Safety Evaluation of Automated Vehicles

Automated Vehicles (AVs) need to be thoroughly evaluated in order to ensure their driving capabilities. However, comprehensive evaluations are intractable due to both time and monetary costs. To address this problem, we propose an Adaptive Design of Experiments (ADOE) method to evaluate the safety of AVs. Using this method, a Surrogate Model (SM) is established and updated iteratively. SM in ADOE is used to approximate the results of AV testing and help to select the next concrete scenario to be tested in each iteration. Two different ADOE approaches are proposed in this study for different testing purposes. Since the choice of the surrogate model has a profound impact on the performance of the ADOE method, 6 surrogate models were compared with two logical scenarios at different scales – a car following logical scenario and a cut-in logical scenario. Results show that Extreme Gradient Boosting (XGB) is suitable for both ADOE approaches. And both proposed ADOE approaches achieved desired performance. Scenario-oriented ADOE made full use of each concrete scenario, capturing one collision case for every 1.12 test runs in the car following logical scenario, while SM-oriented ADOE successfully depicted the boundary between safety and danger. Using 0.46% test resources compared to enumeration, the SM-oriented ADOE found 93.9% dangerous scenarios with 90.9% precision. ADOE approaches have great potential in accelerating the evaluation of AV safety.

Intersection two-vehicle crash scenario specification for automated vehicle safety evaluation using sequence analysis and Bayesian networks

This paper introduces a test scenario specification procedure using crash sequence analysis and Bayesian network modeling. Intersection two-vehicle crash data was obtained from the 2016–2018 National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS) database. Vehicles involved in the crashes are specifically renumbered based on their initial positions and trajectories. Crash sequences are encoded to include detailed pre-crash events and concise collision events. Based on sequence patterns, the crashes are characterized as 55 types. A Bayesian network model is developed to depict the interrelationships among crash sequence types, crash outcomes, human factors, and environmental conditions. Scenarios are specified by querying the Bayesian network’s conditional probability table. Distributions of operational design domain (ODD) attributes (e.g., driver behavior, weather, lighting condition, intersection geometry, traffic control device) are specified based on conditions of sequence types. Also, distribution of sequence types is specified on specific crash outcomes or combinations of ODD attributes.

Development of a Framework for Generating Driving Safety Assessment Scenarios for Automated Vehicles.

Despite the technological advances in automated driving systems, traffic accidents involving automated vehicles (AVs) continue to occur, raising concerns over the safety and reliability of automated driving. For the smooth commercialization of AVs, it is necessary to systematically assess the driving safety of AVs under the various situations that they potentially face. In this context, these various situations are mostly implemented by using systematically developed scenarios. In accordance with this need, a scenario generation framework for the assessment of the driving safety of AVs is proposed by this study. The proposed framework provides a unified form of assessment with key components for each scenario stage to facilitate systematization and objectivity. The performance of the driving safety assessment scenarios generated within the proposed framework was verified. Traffic accident report data were used for verification, and the usefulness of the proposed framework was confirmed by generating a set of scenarios, ranging from functional scenarios to test cases. The scenario generation framework proposed in this study can be used to provide sustainable scenarios. In addition, from this, it is possible to create assessment scenarios for all road types and various assessment spaces, such as simulations, proving grounds, and real roads.