Automated Driving Systems Research Articles

Object detection analysis based on Machine Learning Algorithms

The branch of artificial intelligence known as computer vision focuses on developing and utilizing digital systems to process, examine, and interpret visual data. Computer vision aims to make it possible for computing equipment to accurately recognise an object's digital image and perform the necessary action.The field of transportation and artificial intelligence includes vehicle discovery, which is pivotal. Intelligent transport system (ITS) is a fashion used to produce an effective road transportation system. The Automated Driving System which has made significant advancements in recent times, heavily relies on vehicle identification in its armature. The fashion of detecting vehicles on the road is used for objects similar as vehicle shadowing, counts, and average speed of each vehicle, business analysis, and vehicle categorization and can be applied in a variety of settings. A wide, active, and grueling field of computer vision is real- time object discovery. This recognizes semantic objects of a particular class in digital prints and pictures. For face recognition, vehicle recognition, rambler counting, web image analysis, security systems, and tone- driving motorcars, object discovery is constantly employed. Key Words: Vehicle dataset, ITS, Vehicle detection, Highway management

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>

Online Maneuver Learning and Its Real-Time Application to Automated Driving System for Obstacles Avoidance

Learning methods to adapt the planned path to individual drivers have been proposed to improve the comfort and trust provided by automated driving systems (ADSs). However, existing methods apply offline learning, even if they can accept the request to learn while driving (on-demand learning). Although several online learning methods are available, their learning results have not been applied in real-time for vehicle maneuvering. Focusing on obstacle avoidance, we propose on-demand online learning of preferred paths for individual drivers and investigate whether the proposed method improves the comfort and trust after learning. Unlike the existing methods, the proposed ADS can smoothly and arbitrarily transition between automated and manual driving, thereby learning preferred maneuvers whose results can be applied in real-time to obstacle avoidance. Accordingly, the proposed ADS includes mutual transfer of steering authority between the ADS and driver and curve modification according to the error between the planned path and actual vehicle trajectory. Experimental results show that the proposed ADS method improves the comfort and trust of drivers. In addition, the proposed ADS method learns the paths preferred by individual drivers during avoidance maneuvers to some extent and gradually adjusts the path according to the drivers preference through repeated learning.

Goal-Aware RSS for Complex Scenarios via Program Logic

We introduce a <i>goal-aware</i> extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement&#x2014;in addition to collision avoidance as in the original RSS&#x2014;requires complex planning over long sequences of manoeuvres.To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic <inline-formula><tex-math notation="LaTeX">$\text{dFHL}$</tex-math></inline-formula> that accommodates continuous dynamics and safety conditions. Our framework presents a <inline-formula><tex-math notation="LaTeX">$\text{dFHL}$</tex-math></inline-formula>-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.

Problem Issues of Legal Regulation of the Operation of Cars with an Automated Driving System

In the context of a fundamental change in the fundamental approaches to building a traffic management system, traditionally based on establishing the driver’s duty to ensure constant control over the traffic situation and, accordingly, presuming his responsibility for harm caused by a source of increased danger, the problem of legal regulation of the use of highly automated vehicles equipped with an automated driving system that does not provide for participation of the driver in the dynamic control of the car becomes not only relevant in theoretical, but also especially significant from practical aspects. The objective of the comprehensive study being conducted by the authors was to identify and visualise key groups of problems of legal regulation of the operation of cars with an automated driving system, to formulate proposals for their solution as part of a subsequent systemic legal study. This article is devoted to the results of consideration of the first block of the identified issues. Using the methods of the system-legal approach, formal-logical and formal-dogmatic analysis, the authors have identified the most problematic issues of legalising the terminology used in positive law and scientific sources. In particular, options for identifying the essential features of highly automated cars are proposed with the purpose to further legislatively determine the cars that should be classified as highly automated, to reveal which software and hardware complex can be considered an automated driving system, etc. Based on the results of solving the scientific problem, which consists in determining the directions for adapting the legislation governing the requirements for safety of vehicles and the procedure for their admission to operation for the needs of the widespread introduction of highly automated vehicles, the research can be carried out in two directions at the same time: to develop upper-level, essential requirements to safety and to develop specific, purely technical requirements for automated driving systems, as well as to develop a methodology for testing them. As a part of the taxonomic analysis carried out by the authors to determine, on a fundamentally new basis, the range of rights and obligations of the participants in the relations under the study, it is proposed to highlight the problem of distinguishing between situations in which the driver needs to take an active part in driving a car from situations in which he is not required to be actively involved. into this process. When considering issues of liability for harm caused by a car with an automated driving system, the article focuses on the need to solve the problem of balancing the responsibility of the car owner and the manufacturer, which can be facilitated by the application of methods of comparative legal analysis.

Model predictive eco-driving control of internal-combustion-engine vehicles equipped with CVTs in car-following scenarios

Eco-driving control is a key technology for achieving carbon neutrality of vehicles equipped with automated-driving systems and advanced driver-assistance systems. This paper proposes a model-predictive eco-driving controller for car-following by internal-combustion-engine vehicles equipped with continuous variable transmissions. The effectiveness of our controller is demonstrated through numerical simulations. The results indicate that driving operations like the pulse-and-glide strategy are still candidate optimal solutions to the eco-driving control problem in car-following scenarios even in the receding-horizon setting. Moreover, the results indicate that restricting the engine operation range to the optimal brake-specific-fuel-consumption line is not necessarily optimal in the receding-horizon setting.

Impact of duration of monitoring before takeover request on takeover time with insights into eye tracking data

Safety has become the primary concern of automated driving system (ADS) in recent years. Compared with highly automated driving (L4 and above), conditionally automated driving (L3/L3+ ADS) seems to be a moderate choice, where drivers are required to respond to the takeover request (TOR) whenever necessary. It is the system’s responsibility to make sure that the takeovers would be safe at the time of issuing the TOR. To realize that, a lot of factors need to be taken into consideration. As it has been found that drivers’ eyes-on-road gazes increase slowly in the first few seconds while transferring to manual driving from automated driving and drivers’ gaze behaviors are related with situation awareness, the main aim of this study is to investigate the impact of duration of monitoring before the TOR on takeover time and whether there is a positive or negative relationship between the two. To verify these, we designed a driving simulator study where the TOR was issued 0 s, 5 s, 10 s and ≥ 15 s after the non-driving-related task has ended. Twelve scenarios were designed, and the results from 36 participants showed that there was indeed a statistically significant difference, however, the relationship was neither positive nor negative, which was close to a parabola. Analyzing results of eye movements and gaze behavior further supported this conclusion. It is therefore concluded the duration of monitoring before the TOR should neither be too short nor too long, and 5–7 s would be appropriate choices. This is desirable not only for improving takeover performance of drivers but also for improving the prediction model for predicting takeover performance of drivers that has yet to be studied, so as to improve safety, reliability and acceptance of the ADS.

Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

Differential Game-Based Optimal Control of Autonomous Vehicle Convoy

Group control of connected and autonomous vehicles on automated highways is challenging for the advanced driver assistance systems (ADAS) and the automated driving systems (ADS). This paper investigates the differential game-based approach to autonomous convoy control with the aim of deployment on automated highways. Under the noncooperative differential games, the coupled vehicles make their decisions independently while their states are interdependent. The receding horizon Nash equilibrium of the linear-quadratic differential game provides the convoy a distributed state-feedback control strategy. This approach suffers a fundamental issue that neither a Nash equilibrium’s existence nor the uniqueness is guaranteed. We convert the individual dynamics-based differential game to a relative dynamics-based optimal control problem that carries all the features of the differential game. The existence of a unique Nash control under the differential game corresponds to a unique solution to the optimal control problem. The latter is shown, as well as the asymptotic stability of the closed-loop system. Simulations illustrate the effectiveness of the presented convey control scheme and how it well suits automated highway driving scenarios.

ReTSN: Resilient and Efficient Time-Sensitive Network for Automotive In-Vehicle Communication

Time-sensitive networking (TSN) is being widely investigated to provide Ethernet capabilities for in-vehicle backbone communication. However, the gate control list (GCL), as a simple mechanism for achieving timing determinism for safety-critical traffic (ST) frames with hard deadlines, is too rigid to handle the intrinsic timing uncertainty of automated driving systems (ADSs). Due to the complexity and the unpredictable operating environment, there can be delayed ST frames that can disrupt the solidly fixed timing behavior. In this work, we present one novel approach to effectively use bandwidth resources to deal with delayed ST frames, which cannot be handled by a traditional fixed GCL, and the discarding of them should be reduced to improve the system’s integrity. An acceptance test is implemented to report to the application layer when an ST frame will miss its deadline and hence be rejected, i.e., prevented from entering the switch. To further improve the efficiency of bandwidth usage, we investigate how to improve the performance of the more important Class A frames in an audio-video-bridging (AVB) switch, which adopts a credit-based shaper mechanism, and we propose a constant bandwidth server to replace the credit-based shaper while taking fairness into consideration. Evaluation with extensive experiments shows that both resilience and efficiency of the TSN are significantly enhanced compared with a credit-based shaper, especially when the traffic load is relatively high. For the delayed ST frames and event-triggered traffic, our approach is able to schedule more than the solution using the AVB switch even with a high network load.

Multimodal interface and reliability displays: Effect on attention, mode awareness, and trust in partially automated vehicles.

The goal of this study is to evaluate the effect of a multimodal interface indicating the limits of automation in order to stimulate an appropriate level of attention and to induce accurate mode awareness and trust in partial driving automation. Participants drove in a driving simulator with partial driving automation and were confronted with surprising situations of suspension of driving automation systems in different contexts. They drove the simulator during three driving sessions, with either a multimodal interface indicating the limits of automation or a visual basic interface. Their driving performance, ocular behavior, and subjective evaluation of trust and workload were evaluated. The results revealed that the multimodal interface stimulates an appropriate level of attention and increases mode awareness and trust in automation, but these effects are context-dependent. The indications of the limits of automation improved the knowledge regarding automation, but this knowledge did not necessarily lead to improved driving performance. Design solutions are discussed to support the improvement of driving performance for take-overs in vehicles equipped with partial driving automation.

When Automation Fails: Examining the Effect of a Verbal Recovery Strategy on User Experience in Automated Driving

Automated agents’ errors will cause various negative influences on humans and their relationships with humans (e.g., reducing user experience). They are increasingly required to have social recovery strategies (e.g., human-like apology and explanation) to mitigate the negative impacts of their errors and maintain resilient human–automation relationships. However, the efficacy of these strategies in human–automation interaction (HAI) largely remains unknown, especially in less controlled environments. Here we conducted a test track experiment and designed a verbal recovery design (consisting of an apology, explanation, and promise) by an automated driving system (ADS) installed in a real automated vehicle after an ADS failure. We utilized a Wizard of Oz design to simulate the ADS’ failure and its verbal recovery attempt (through a voice by a human or Apple Siri). Participants (N = 389) were assigned to four groups: normal (without experiencing the ADS failure), fault (experiencing the ADS failure), Siri-voice-recovery, and human-voice-recovery. The major measures were positive experience and negative experience while riding in the automated vehicle and perceived ADS usability. Overall, we found that the human-voice-recovery can to some degree mitigate the negative impacts of the ADS failure on user experience. The Siri-voice-recovery worked on positive experience but cannot restore it to that in the normal group. It implies that more empirical efforts are needed to examine social recovery strategies in HAI in natural environments, develop strategies specific to HAI, and offer effective guidelines for social recovery design.

Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating traffic disturbances. Many existing ACC and CACC control models focus on three strategies that can indirectly mitigate congestions and damp traffic shockwaves: time headway suppression, stability improvement, and adaptive mode-switching control based on traffic conditions. Although congestion mitigation and shockwave damping are achievable with those control strategies, the improvement is often limited to the by-product effect of their main control objectives of headway suppression and system stability. Many adaptive models also need to calibrate many parameters for different road conditions.This paper proposes a new ACC and CACC framework that directly integrates the congestion shockwave damping and traffic congestion mitigation into the objective function of a dynamic control framework. A Model Predictive Control (MPC) based dynamic optimization framework is proposed to balance the trade-offs among multiple objectives, including safety, efficiency, shockwave, elasticity, and driver’s comfort. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models are derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.

Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment

This study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encountering a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle. We extracted the cut-out scenarios from an established real-world traffic dataset recorded by instrumented vehicles on Japanese highways and then defined using vehicle kinematic parameters (velocities and distances). The extracted scenarios were analyzed based on the direct correlation between every two consecutive vehicles: a rear part that describes the correlation between the following vehicle and the cutting-out vehicles; and a frontal part that describes the correlation between the cutting-out vehicle and the preceding vehicle. Parameter ranges were quantified with a regression model and determined based on the risk acceptance threshold applied in the field of Japanese high-speed trains and annual exposure by professional highway drivers to produce a scenario space with a reasonably foreseeable range in which ADS may not produce crashes lest it performs worse than human drivers. A multi-dimensional distribution analytical approach was used to derive a correlation between the following vehicle and the preceding vehicle considering the initial longitudinal velocities. Results suggest that there should be no risk of a collision between the following vehicle and the preceding vehicle when the time headway between the following vehicle and the cutting-out vehicle is equal to or more than 2 s. These findings can help to understand normative driver behavior during cut-out scenarios and to generate accident-free scenario space for which ADS must perform flawlessly.

ADSCENE scenarios data base: Focus on accident data support for validation of Automated Driving functions

Automated systems such as SAE AD level 2 and higher, face the difficult challenge of proving safe in the field, in actual traffic conditions. Scenario-based simulation approaches are, therefore, necessary complements to the traditional approach, allowing computation of a controlled diversity of key variables in many iterations in a safe, fast, and documented way. ADSCENE capitalizes scenarios to be considered during the design & validation phases of Advanced Driving Assistance Systems as well as Automated Driving systems into a common data base. Several data sources are used to fill in ADSCENE: near crash data, normal driving data, scenarios defined by experts in AD/S ADAS, scenarios from the regulations. This paper describes how accident data complements the scope of the other sources and the methodology used to extract and code relevant accidents into ADSCENE.

Regulation requirements on automated driving systems in unsignalized intersections

A new EU regulation is being drafted to lay down the technical specifications concerning type-approval of Automated Driving System (ADSs). One of the requirements included in the regulation concerns the conditions for merging or crossing unsignalized intersections in the presence of oncoming privileged traffic. The requirements have been derived by studying the gap that human drivers usually accept in the same case. In particular, it has been found that a minimum separation in terms of Time to Collision (TTC) is required for the whole manoeuvre and it is demonstrated that only a percentage of the most aggressive drivers accept gaps that are smaller than what is required by the regulation.

Ruling the Operational Boundaries: A Survey on Operational Design Domains of Autonomous Driving Systems

Automated driving systems (ADS) have the potential to offer a safe and efficient future for mobility. At the beginning of the design process of an ADS, the operational limits have to be defined using the operational design domain (ODD). Nonetheless, the field of ODD has only become popular recently, and the necessary regulations, standards, and development approaches are still emerging. Current research contributions in the domain of ODD have fragmented in recent years and have not followed a concrete direction. This survey examines a large proportion of the recent research in the domain of ODD by systematically identifying subject areas and categorising relevant publications, thereby integrating the approaches and showing an overview of the emerging topic area. Furthermore, it identifies existing gaps in the ODD research that need to be considered. Finally, the paper suggest relevant future development of ODD.

A Review of the Impact of Rain on Camera-Based Perception in Automated Driving Systems

A Review of the Impact of Rain on Camera-Based Perception in Automated Driving Systems

Operational Design Domain-Driven Coverage for the Safety Argumentation of Automated Vehicles

Automated Vehicles aim to increase road safety as automated driving systems (ADS) take over the human driving task in the operational design domain (ODD), introducing severe challenges for safety validation. Pure driving over many kilometers to gather enough evidence for a safety argument is not feasible. Scenario-based testing is an approach to overcome this, but challenges like parameter discretization still prevail, hindering safety assurance. This work proposes contributions towards a traceable and efficient safety argumentation for ADS built upon ODD coverage. First, ODD coverage is thoroughly quantified across all scenario levels, assuming distribution functions’ availability for the scenario parameters. Secondly, a sampling method for n-dimensional scenario parameter distributions is proposed. The provided algorithms adapt an initial k-means clustering using pre-defined boundary conditions requiring significantly fewer scenarios. Furthermore, a risk metric for urban intersections is presented for scenario evaluation. The risk metric consists of two parts, scene prediction of traffic participants (TPs) and risk assessment. The scene prediction uses a manoeuvre-based motion model with a data-driven approach towards trajectory prediction, increasing the validity. For the risk assessment, a probabilistic risk prediction for the TPs is performed for each scenario scene. The risk metric shows a reasonable tradeoff between sensitivity and specificity, outperforming time-to-collision. These contributions are exemplarily applied at an intersection using a simplified setup for generating TPs and ego vehicle trajectories. The results indicate that an increased safety argumentation is enabled using the proposed methods alongside a coverage process, facilitating further research.

Determining the Validity of Simulation Models for the Verification of Automated Driving Systems

Determining the Validity of Simulation Models for the Verification of Automated Driving Systems