Automated Driving Research Articles

Safety of the Intended Functionality Concept Integration into a Validation Tool Suite

Nowadays, the increasing complexity of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) means that the industry must move towards a scenariobased approach to validation rather than relying on established technology-based methods. This new focus also requires the validation process to take into account Safety of the Intended Functionality (SOTIF), as many scenarios may trigger hazardous vehicle behaviour. Thus, this work demonstrates how the integration of the SOTIF process within an existing validation tool suite can be achieved. The necessary adaptations are explained with accompanying examples to aid comprehension of the approach.

Examining the Effect of Collaboration Effort, Input Method, and Age on Driver–Automation Collaboration in Unstructured Driving Environments

Both drivers and the current state of advanced driver-assistance systems (ADASs) are imperfect, but their collaboration as a team can compensate for individual limitations. Unstructured driving environments (e.g., parking lots and off-road trails) entail greater challenges for an ADAS and require driver–automation collaboration. However, few studies describe the factors shaping drivers’ subjective experience of collaboration with driving automation in unstructured environments. In this study, we examined the effects of collaboration effort and input method on drivers’ subjective experience, including mental workload, trust, and perceived usability. In addition, we examined how collaboration is experienced differently across different age groups. In this driving simulation study, participants collaborated with a hypothetical ADAS to navigate two unstructured driving environments. The results indicate that collaboration effort and input method did not directly affect drivers’ collaboration experience. However, examining the effect of collaboration effort by driver age suggests that older drivers (55 years old and over) experienced higher mental workload and gave lower usability ratings to the collaboration method than younger drivers. These results imply that older drivers may experience more challenging collaboration than younger drivers when they must devote substantial cognitive effort to collaborate with automation. The findings suggest designing driver–automation collaboration features to reduce cognitive effort for older drivers navigating unstructured driving environments. Moreover, future research must examine drivers’ collaboration experiences based on their unique situations, shaped by the interplay between situational properties (e.g., task demands) and driver characteristics (e.g., age) rather than relying on a limited understanding of individual factors.

Driving Domain Classification Based on Kernel Density Estimation of Urban Land Use and Road Network Scaling Models

Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas.

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process.

Does in-vehicle automation help individuals with Parkinson's disease? A preliminary analysis.

PD is a progressive neurodegenerative disorder that affects, according to the ICF, body systems (cognitive, visual, and motor), and functions (e.g., decreased executive functions, decreased visual acuity, impaired contrast sensitivity, decreased coordination)-all which impact driving performance, an instrumental activity of daily living in the domain of "Activity" and "Participation" according to the ICF. Although there is strong evidence of impaired driving performance in PD, few studies have explored the real-world benefits of in-vehicle automation technologies, such as in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), for drivers with PD. These technologies hold potential to alleviate driving impairments, reduce errors, and improve overall performance, allowing individuals with PD to maintain their mobility and independence more safely and for longer periods. This preliminary study aimed to fill the gap in the literature by examining the impact of IVIS and ADAS on driving safety, as indicated by the number of driving errors made by people with PD in an on-road study. Forty-five adults with diagnosed PD drove a 2019 Toyota Camry equipped with IVIS and ADAS features (Toyota Safety Sense 2.0) on a route containing highway and suburban roads. Participants drove half of the route with the IVIS and ADAS systems activated and the other half with the systems deactivated. The results suggest that systems that assume control of the driving task, such as adaptive cruise control, were most effective in reducing driving errors. Furthermore, individual differences in cognitive abilities, particularly memory, were significantly correlated with the total number of driving errors when the systems were deactivated, but no significant correlations were present when the systems were activated. Physical capability factors, such as rigidity and bradykinesia, were not significantly correlated with driving error. Taken together, these results show that in-vehicle driver automation systems can benefit drivers with PD and diminish the impact of individual differences in driver cognitive ability.

Cooperative Game Theory based robust shared lateral control for Driver Vehicle Interactions

Extensive manual driving is liable to attention lapses and driver exhaustion resulting in lane offset errors, driver comfort minimization and degradation in vehicle stability. On the contrary, apart from being expensive, autonomous vehicles suffer from the issue of trust, ethics and traffic mapping. In order to address the above limitations, this paper deals with the design of a Game Theoretic based Shared Control (GTSC) framework that enables an interaction between the human and the machine. The novelty lies in the generation of automatic driver automation relative weights based on the vehicle’s lateral position relative to the maximum admissible lateral offset. The interaction between the human driver and automated system based on the cooperative game theory is utilized to develop a robust shared control framework. The driver model parameters have been estimated using the experimentally obtained driver steering torque and steering wheel angle respectively through an inverse modelling procedure. The lateral dynamics of the vehicle model in the presence of crosswinds and yaw moment as disturbances are considered for the design. A higher order sliding mode observer is designed to ensure robust estimation of the unknown states. Unlike the existing studies that primarily concentrates on the robust game theoretic shared controller design, this new study broadens its focus. The study introduces an observer-based robust design for GTSC and establishes the stability of the closed-loop system. The effectiveness of this proposed framework is verified through simulations in Carsim as well as real-world testing on a Hardware-in-the-loop (HIL) platform with a human driver. The study also addresses the challenge of obstacle avoidance using the GTSC framework. Comparisons have been performed with the state-of-the-art shared control studies while considering factors like crosswinds, variation in the longitudinal velocities and alterations in the reference path to evaluate the effectiveness of the proposed shared control study. The results demonstrate the improvement in the lateral offset, driver comfort and the vehicle stability compared to the manual control of the vehicle.

Framing Updates: How Framing Influences Trust for Automated Driving Systems

The boom of automated driving systems (ADS) promises to change the way humans drive and interact with their vehicle, especially when these systems receive new updates that may change the way they work. Human-automation teams need to ensure proper roles are established for who is in control of the driving task at any given time. The human needs to have properly calibrated trust to know how to properly work with the system during driving. Framing research shows that positive and negative framing can influence how individuals perceive and make decisions, and swift trust shows that trust can be created quickly in newly established teams. We draw from both realms of literature and tested how new updates of the ADS are framed to the driver with the goal of either promoting or dampening trust to ensure the human driver is maintaining proper trust calibration.

Fostering Drivers' Trust in Automated Driving Styles: The Role of Driver Perception of Automated Driving Maneuvers.

This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers' perception of automated driving maneuvers and quantified the relationships among drivers' perception of AV maneuvers, driver trust, and acceptance of AVs. Previous studies on automated driving styles focused on the impact of AV's global driving style on driver's attitude and driving performance. However, research on drivers' perception of automated driving maneuvers at the specific driving style level is still lacking. Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator. Results revealed that drivers' trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn't differ significantly in their perceived inappropriateness of these maneuvers with different driving styles. The driving styles of automated vehicles and drivers influenced drivers' perception of automated driving maneuvers, which influence their trust and acceptance of AVs. This study suggested that the design of AVs should consider drivers' perceptions of automated driving maneuvers to avoid undermining drivers' trust and acceptance of AVs.

Implications of the positive risk balance on the development of automated driving

Objectives Automated driving (AD) from SAE level 3 onwards represents a paradigm change from human driver controlling the vehicle to a technical system controlling it. In this light, different regulatory bodies (European Commission, Germany, etc.) have defined guidelines for the operation of such a system. One core principle of these guidelines is that the automated operation needs to be at least as safe as human driving—often referred to as the “positive risk balance.” However, these guidelines are general and do not provide details on what this means in a practical sense. This article discusses a method to demonstrate how positive risk balance can be addressed in practice. Methods Starting from a detailed analysis of corresponding guidelines and a literature review of possible risk assessment frameworks, a comprehensive approach has been developed to consider ethical requirements for the development of AD. This approach covers different development stages. The PrOACT-URL (Problems, Objectives, Alternatives, Consequences, Trade-offs, Uncertainty, Risk attitudes, and Linked decisions) approach was chosen for reporting of the work. Results The article will present the approach developed by BMW to ensure that a positive risk balance is achieved for an AD system. The approach is presented per development stage (concept phase, AD development phase, verification and validation phase, post–start of production phase). In the concept phase, the scope is to define how good a human driver is and how good an AD needs to be. In the AD development phase, first the relevant system requirements need to be derived. Monte Carlo experiments in combination with Bayesian networks are applied. The fulfillment of these requirements is checked in the verification phase through simulations and test track and real-world tests. For validation of the risk balance, the impact of AD in terms of traffic safety is derived by means of simulation. In the post–start of production phase, field observation is used. Conclusion The safety of AD is paramount when it comes to its operation and ensuring trust in this technology. The described approach contributes directly to building this trust by considering the principle of a positive risk balance throughout the development in addition to existing safety standards for advance driver assistance systems, such as ISO 26262, ISO21434 or ISO 21488.

Design of a Secure Automated Driving Systems Test Data Interface

<div class="section abstract"><div class="htmlview paragraph">Vehicles equipped with Level 4 and 5 autonomy will need to be tested according to regulatory standards (or future revisions thereof) that vehicles with lower levels of autonomy are currently subject to. Today, dynamic Federal Motor Vehicle Safety Standards (FMVSS) tests are performed with human drivers and driving robots controlling the test vehicle’s steering wheel, throttle pedal, and brake pedal. However, many Level 4 and 5 vehicles will lack these traditional driver controls, so it will be impossible to control these vehicles using human drivers or traditional driving robots. Therefore, there is a need for an electronic interface that will allow engineers to send dynamic steering, speed, and brake commands to a vehicle. This paper describes the design and implementation of a market-ready Automated Driving Systems (ADS) Test Data Interface (TDI), a secure electronic control interface which aims to solve the challenges outlined above. The interface consists of a communication port integrated into automobiles which lack traditional manual controls. Via this interface dynamic test scenarios can be executed by plugging in a Vehicle Control Unit (VCU), which is an element of the TDI hardware/firmware intended to be available only to authorized users. Physically, the VCU can interface with the On-board Diagnostics (OBD) OBD-II connector that is present on essentially all modern automobiles. This paper also describes a demonstration of the complete VCU with the secure TDI using a vehicle equipped with a Dataspeed drive-by-wire kit, with its steering, throttle, and brake control systems mimicking the behavior of an autonomous subject vehicle.</div></div>

Ethical and legal challenges of automated driving: The prioritization of socio-political values

Much research on the ethics of automated driving (AD) focusses on moral decision-making processes in extreme traffic situations. However, more likely scenarios involving daily trade-offs among different values in the design and implementation of AD are also worth considering. Our main goal was to provide an in-depth analysis of how diverse experts and stakeholders prioritize different socio-political values. Through a series of workshops, semi-structured interviews, and focus groups, we assessed today's major social, ethical, and legal concerns related to AD, as well as their relative prioritization in specific near-future realistic traffic scenarios. We summarize experts’ and lay-people's opinions on the trade-offs among six socio-political values (privacy, autonomy, safety, security, performance, and costs) and present overall recommendations for the design of future AD technologies.

Trustworthy Automated Driving through Increased Predictability: A Field-Test for Integrating Road Infrastructure, Vehicle, and the Human Driver

Higher levels of Automated driving (AD) vehicles require new allocations of functions among drivers, vehicles, and road infrastructure. The European Horizon 2020 project HADRIAN investigates how such reallocations could be practically achieved as part of Collaborative Connected and Automated Mobility (CCAM) to meet the benefit expectations of drivers while increasing safety. In a field demonstration it is shown how road infrastructure can be used to expand the prediction horizon of AD vehicles and how multimodal, driver-state dependent human machine interactions (HMI) could help address European mobility needs with AD vehicles and increase operational acceptance and safety. Whereas performance results of the various innovations are reported elsewhere, in this paper the evaluation of the feasibility of the HADRIAN innovation in an open road field-demonstration is described.

Formal Development of Safe Automated Driving Using Differential Dynamic Logic

The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment. Conventional development approaches such as testing and simulation are limited by non-exhaustive analysis, and can thus not guarantee safety in all possible scenarios. Formal methods can provide mathematical proofs that could be used to produce rigorous evidence to support the safety argument. This paper investigates the use of differential dynamic logic and the deductive verification tool KeYmaera X in the development of an AD feature. Specifically, this paper demonstrates how formal models and safety proofs of different design variants of a Decision & Control module can be used in the safety argument of an in-lane AD feature. In doing so, the assumptions and invariant conditions necessary to guarantee safety are identified, and the paper shows how such an analysis helps during the development process in requirement refinement and formulation of the operational design domain. Furthermore, it is shown how the performance of the different models is formally analyzed exhaustively, in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> their allowed behaviors.

Investigating the Potential of a Scenario Catalogue for Automated Driving Safety Evaluation to Cover Real-World Crashes

Investigating the Potential of a Scenario Catalogue for Automated Driving Safety Evaluation to Cover Real-World Crashes

Teaming with Your Car: Redefining the Driver–Automation Relationship in Highly Automated Vehicles

Advances in automated driving systems (ADSs) have shifted the primary responsibility of controlling a vehicle from human drivers to automation. Framing driving a highly automated vehicle as teamwork can reveal practical requirements and design considerations to support the dynamic driver–ADS relationship. However, human–automation teaming is a relatively new concept in ADS research and requires further exploration. We conducted two literature reviews to identify concepts related to teaming and to define the driver–ADS relationship, requirements, and design considerations. The first literature review identified coordination, cooperation, and collaboration (3Cs) as core concepts to define driver–ADS teaming. Based on these findings, we propose the panarchy framework of 3Cs to understand drivers’ roles and relationships with automation in driver–ADS teaming. The second literature review identified main challenges for designing driver–ADS teams. The challenges include supporting mutual communication, enhancing observability and directability, developing a responsive ADS, and identifying and supporting the interdependent relationship between the driver and ADS. This study suggests that the teaming concept can promote a better understanding of the driver–ADS team where the driver and automation require interplay. Eventually, the driver–ADS teaming frame will lead to adequate expectations and mental models of partially automated vehicles.

Frameworks for assessing societal impacts of automated driving technology

ABSTRACT Numerous studies have studied the impacts of automated driving (AD) technology on e.g. accident rates or CO2 emissions using various frameworks. In this paper we present an overview of previous frameworks used for societal impacts and review their advantages and limitations. Additionally, we introduce the Total Impact Assessment (TIA) framework developed by the Swedish Transport Administration and use this framework to evaluate three scenarios for AD bus services in Stockholm. We conclude that the reviewed frameworks cover different aspects of AD technology, and that e.g. cybersecurity and biodiversity are areas largely neglected. Furthermore, most frameworks assume effects to be homogenous, when there may be large variation in e.g. perceived security. The TIA framework does not manage to include all societal aspects of AD technology, but has great benefits and manages to provide important insights of the societal impacts of AD technology, especially how effects may wary for different actors.

Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments.

High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy.

Wheeled Vehicle Detection Under the Asymmetric Micro-Doppler Interference

With the development of automated driving (AD) applications, the micro-Doppler effect induced by the rolling wheels of the vehicles normally disturbs the target detection and the effect is difficult to be eliminated at close range. In this article, by illustrating the micro-Doppler motion of the rolling wheels, the asymmetric Doppler spectrum caused by the micro-Doppler interference is innovatively introduced. A random-finite-set (RFS)-based measurement model is applied to analyze the asymmetry. To eliminate the asymmetric phenomenon, a temporal smoothing method based on the cadence velocity diagram (CVD) is proposed. Simulations based on the RFS model and experiments prove the exhibition of the asymmetric micro-Doppler interference introduced by rolling wheels and the feasibility of the proposed elimination method. Moreover, compared with the state-of-the-art symmetry method, the detection probability improves from 80% to 93% when the false alarm rate is fixed at 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> , showing that the proposed method is highly efficient and the false detection caused by the micro-Doppler effect can be eliminated accurately. The results may provide a new way for high-quality wheeled vehicle detection.

On-Road Assessment of Driver Mode Awareness of Assisted and Automated Driving

Increasingly, today’s vehicles offer Society of Automotive Engineers (SAE) Level 2 (L2) technology, and a limited number of SAE Level 3 (L3) automated driving systems (ADSs) are being developed. Since L3 ADSs can operate in L2 and manual driving, drivers’ correct mode awareness is essential. To investigate driver’s mode awareness after transitions between modes, an on-road experiment was conducted using a Wizard-of-Oz method to simulate Assisted Driving (L2-like) and Automated Driving (L3/L4-like). A total of 36 drivers completed the on-road experiment. Participants experienced periods of manual driving, assisted driving, automated driving, and transitions between these modes. After each transition, participants’ mode awareness was measured. It was hypothesized that drivers’ mode awareness would increase with system exposure over the planned automated driving periods. Results showed that drivers exhibited declarative mode awareness during both Assisted (ASD) and Automated Driving (AD) modes.

Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-Based Convolutional Neural Networks

Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records. Thus, reliably predicting such manoeuvre in advance is critical for the safe and comfortable operation of automated driving systems. The majority of previous studies rely on detecting a manoeuvre that has been already started, rather than predicting the manoeuvre in advance. Furthermore, most of the previous works do not estimate the key timings of the manoeuvre (e.g., crossing time), which can actually yield more useful information for the decision making in the ego vehicle. To address these shortcomings, this paper proposes a novel multi-task model to simultaneously estimate the likelihood of LC manoeuvres and the time-to-lane-change (TTLC). In both tasks, an attention-based convolutional neural network (CNN) is used as a shared feature extractor from a bird's eye view representation of the driving environment. The spatial attention used in the CNN model improves the feature extraction process by focusing on the most relevant areas of the surrounding environment. In addition, two novel curriculum learning schemes are employed to train the proposed approach. The extensive evaluation and comparative analysis of the proposed method in existing benchmark datasets show that the proposed method outperforms state-of-the-art LC prediction models, particularly considering long-term prediction performance.