Safety is the cornerstone of L2+ autonomous driving and one of the fundamental tasks is forward collision warning that detects potential rear-end collisions. Potential collisions are also known as conflicts, which have long been indicated using Time-to-Collision with a critical threshold to distinguish safe and unsafe situations. Such indication, however, focuses on a single scenario and cannot cope with dynamic traffic environments. For example, TTC-based crash warning frequently misses potential collisions in congested traffic, and issues false alarms during lane-changing or parking. Aiming to minimise missed and false alarms in conflict detection, this study proposes a more reliable approach based on vehicle spacing patterns. To test this approach, we use both synthetic and real-world conflict data. Our experiments show that the proposed approach outperforms single-threshold TTC unless conflicts happened in the exact way that TTC is defined, which is rarely true. When conflicts are heterogeneous and when the information of conflict situation is incompletely known, as is the case with real-world conflicts, our approach can achieve less missed and false detection. This study offers a new perspective for conflict detection, and also a general framework allowing for further elaboration to minimise missed and false alarms. Less missed alarms will contribute to fewer accidents, meanwhile, fewer false alarms will promote people's trust in collision avoidance systems. We thus expect this study to contribute to safer and more trustworthy autonomous driving.

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AI in automotive is sterk in opkomst. Nieuwe voertuigen zijn always connected en worden zelflerende machines. Zo pakt de techniek een steeds grotere rol in de bediening en besturing van onze voertuigen. Wat betekent dat voor de veiligheid? En verandert dat ook de rol van de RDW?@en

This study proposes a general framework to investigate car-following heterogeneity and its impacts on traffic safety and sustainability. The framework incorporates rigorous driving style classification using a semi-supervised learning technique and a micro-simulation process that includes 66 fine-grained traffic scenarios exhibiting varying degrees of heterogeneity. Validated using two distinct real-world datasets reveals the superiority of S3VM-based classifiers over traditional SVM classifiers in driving style classification. Simulation results show that an increase in driving aggressiveness is correlated with higher safety issues and greater environmental impacts. Further elucidation of these impacts from the mechanism of underlying characteristics of driving behaviour and traffic flow dynamics indicates that less aggressive drivers can lead to the formation of vehicular platoons, thereby encouraging more aggressive drivers to adopt a milder driving style. Importantly, the formation of these platoons is influenced by both the proportion and spatial distribution of less aggressive vehicles. The proposed approach promises advantages in reducing the negative impacts of driving heterogeneity, thus benefiting Intelligent Transportation Systems (ITS) by improving traffic safety and sustainability.

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Noise annoyance and its relation to health outcomes have been studied extensively. The vast majority of studies in this field use cross-sectional data. Such data does not allow investigation of temporal effects or the direction of these effects. It is reasonable to expect that the effects of noise on health build up over time. Moreover, noise may not only impact health outcomes, but health outcomes may also impact the sensitivity to noise – hence flipping the direction of the effect. Further adding to the complexity of the relationship between noise and health outcomes is the fact that personality traits may influence re-ported noise annoyance. This study aims to shed light on the accumulative effects of noise annoyance on health, as well as the bidirectional relationships between noise annoyance and health. To do so, we analyze eight years of data from the Dutch Longitudinal Internet Studies for the Social Sciences (LISS) panel. Specifically, we conduct a Longitudinal Latent Class Analysis to identify annoyance profiles and analyze the effect of Big Five personality traits on the likelihood of belonging to the different annoyance profiles. Furthermore, we use Cross-Lagged Panel Models to analyze whether changes in noise annoy-ance precede changes in health outcomes or vice versa. We find three different profiles of noise annoy-ance, namely chronically annoyed people, occasionally annoyed people, and people who are generally not annoyed. Noise annoyance was found to be relatively stable over time. Regarding personality traits, we find that extraversion and emotional stability decrease the chance of belonging to the cluster of chronically annoyed persons, while openness has the opposite effect. Finally, chronic annoyance shows a significant effect on self-reported heart complaints and sleeping problems, while the effects of noise annoyance profiles on high blood pressure and heart attacks are insignificant.@en

The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how meaningful human control relates to subjective experiences of actual users of these systems. To address this gap, our study aimed to investigate the alignment between the degree of meaningful human control and drivers' perceptions of safety and trust in a real-world partially automated driving system. We utilized previously collected data from interviews with Tesla "Full Self-Driving"(FSD) Beta users, investigating the alignment between the user perception and how well the system was tracking the users' reasons. We found that tracking of users' reasons for driving tasks (such as safe maneuvers) correlated with perceived safety and trust, albeit with notable exceptions. Surprisingly, failure to track lane changing and braking reasons was not necessarily associated with negative perceptions of safety. However, the failure of the system to track expected maneuvers in dangerous situations always resulted in low trust and perceived lack of safety. Overall, our analyses highlight alignment points but also possible discrepancies between perceived safety and trust on the one hand, and meaningful human control on the other hand. Our results can help the developers of automated driving technology to design systems under meaningful human control and are perceived as safe and trustworthy.

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The in-vehicle communication provides promising opportunities to improve the road safety and traffic efficiency. Previous studies demonstrated that the professional drivers have better driving skills than the non-professional drivers who allocate more attention to secondary tasks. However, they may not be sensitive to the new in-vehicle technology. In addition, these qualitative studies failed to elaborate on the visual and response behavior differences among different driver groups (professional drivers such as taxi, bus, motorcoach, and non-professional drivers), and lacked the quantitative analysis of driving patterns in a new environment. This paper explores the differences in visual interaction, response characteristics, driving performance, and behavior patterns between the professional and non-professional drivers in the connected environment through a case study of intersection-approaching behavior using a driving simulator. More precisely, two driving scenarios (baseline and human–machine interface (HMI)) were designed in the driving simulator, and 65 participants, including 34 professional drivers and 31 non-professional drivers, completed the experiment. In the HMI scenario, the driver was provided with the signal light phase and phase transition remaining time of the current intersection. This paper also proposes a driving pattern extraction model based on the Bayesian non-parametric method combined with a text clustering algorithm to perform a quantitative description of the driving patterns. The results show that the professional drivers tend to interact less with the HMI compared with the non-professional drivers. Moreover, the professional drivers’ first gaze at the HMI occurs and responds earlier. The proposed driving model can effectively describe 7 patterns of intersection-approaching behavior. The connected information can significantly improve the efficiency of the intersection traffic and the driving behavior. However, the professional drivers are more responsive and behave more consistently. This study can provide insights into the development of personalized assisted driving systems, as the two driving populations differ in their interactions, responses, and behavioral patterns.

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Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle decision-making and execution control to improve traffic performance. This paper introduces a DRL-based mechanism for cooperative lane changing in mixed traffic (CLCMT) for connected and automated vehicles (CAVs). The uncertainty of human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs are explicitly modelled, and different leader-follower compositions are considered in CLCMT, which provides a high-fidelity DRL learning environment. A feedback module is established to enable interactions between the decision-making layer and the manoeuvre control layer. Simulation results show that the increase in CAV penetration leads to safer, more comfort, and eco-friendly lane-changing behaviours. A CAV-CAV lane-changing scenario can enhance safety by 24.5%–35.8%, improve comfort by 8%–9%, and reduce fuel consumption and emissions by 5.2%–12.9%. The proposed CLCMT promises advantages in the lateral decision-making and motion control of CAVs.

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Integrating Automated Vehicles (AVs) into existing traffic systems holds the promise of enhanced road safety, reduced congestion, and more sustainable travel. Effective integration of AVs requires understanding the interactions between AVs and Human-driving Vehicles (HVs), especially during the transition period in which AVs and HVs coexist in a mixed traffic environment. Numerous recent empirical studies find reduced headways of human drivers following an AV compared to following an HV, and attribute this reduction to behavioural changes of drivers when they follow AVs. However, more factors may be at play due to the inherent inconsistencies between the comparison conditions of HV-following-AV and HV-following-HV. This study scrutinises three alternative explanations for the observed reduction in headways: (1) systematic differences in car-following states during data collection, (2) systematic differences in driving variability between leading AVs and HVs, and (3) systematic differences in driving characteristics of leading AVs versus HVs. We use a large-scale dataset extracted from Lyft AV motion data and examine each of these explanations through data stratification and simulation. Our results show that all three mechanisms contribute to the observed reduction in headways of human drivers following AVs. In addition, our findings highlight the importance of driving homogeneity and stability in achieving reliably shorter headways. Thereby, this study offers a more comprehensive understanding on the difference between HV–AV and HV–HV interactions in mixed traffic, and is expected to promote more effective integration of AVs into human traffic.

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With the increased deployment of autonomous vehicles (AVs) in mixed traffic flow, ensuring safe and efficient interactions between AVs and human road users is important. In urban environments, intersections have various conflicts that can greatly affect driving safety and traffic efficiency. This study uses road test data to examine the possible safety and efficiency impacts of intersection conflict resolution involving AVs. The contribution comprises two main aspects. Firstly. we prepare and open a high-quality lateral conflict resolution dataset derived from the Argoverse-2 data, specifically targeting urban intersections. A rigorous data processing pipeline is applied to extract pertinent scenarios, rectify anomalies, enhance data quality, and annotate conflict regimes. This effort yields 5000+ AV-involved and 16000 AV-free cases, covering rich conflict regimes and balanced traffic states. Secondly, we employ surrogate safety measures to assess the safety impact of AVs on human-driven vehicles (HVs) and pedestrians. In addition, a novel concept of Minimum Recurrent Clearance Time (MRCT) is proposed to quantify the traffic efficiency impacts of AVs during conflict resolution. The results show that, for AV–HV and HV–HV conflict resolution processes, the differences in selected safety and efficiency measures for human drivers are statistically insignificant. In contrast, pedestrians demonstrate diverse behaviour adjustments. Some pedestrians behave more conservatively when interacting with AVs than with HVs. Notably, the efficiency of AV-involved conflict resolution is significantly lower than in AV-free instances due to the conservative driving style of AVs. This efficiency gap is particularly large when AVs pass through the conflict point after human drivers in unprotected left turns. These observations offer a perspective on how AVs potentially affect the safety and efficiency of mixed traffic. The processed dataset is openly available via https://github.com/RomainLITUD/conflict_resolution_dataset.

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Ensuring operational control over automated vehicles is not trivial and failing to do so endangers the lives of road users. An integrated approach is necessary to ensure that all agents play their part, including drivers, occupants, vehicle designers and governments. A comprehensive approach to this problem is possible by considering Meaningful Human Control (MHC). In this chapter, an Integrated System Proximity framework and Operational Process Design approach are introduced to assist the development of Connected Automated Vehicles (CAVs) under the consideration of MHC. The framework includes an extension to a system approach, which also considers ways that MHC can be improved through either Implicit Proximal Updating or Explicit Distal Updating. The implementation is demonstrated using recent cases from practice. Finally, stakeholders are called upon to ensure that MHC processes are explicitly included in policy, regulations, and design processes so that CAVs advance in a responsible, safe and humanly agreeable fashion.

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Conditionally automated driving enables drivers to engage in non-driving-related activities, with the responsibility to take over vehicle control upon request. This takeover process increases the risk of collisions, especially when drivers fail to safely complete takeovers within limited time budgets (i.e., the time offered by automation for takeovers). This phenomenon underlines the significance of providing time budgets that sufficiently accommodate drivers' takeover time (i.e., the time required by drivers to resume conscious control of vehicles). Considering that drivers' takeover time varies significantly across scenarios, this study centres on understanding the role of driver perception in takeover time using the Task-Capability Interface (TCI) theory. The TCI theory suggests that drivers adjust their behaviours based on their perceived task demands and driver capabilities. Accordingly, in a driving simulator experiment featuring diverse traffic densities and distractions, we investigated drivers' takeover time while capturing their perceived task demands and capabilities through a takeover-oriented questionnaire based on established instruments. The results show that drivers generally have longer takeover time as their perceived task demand rises, perceived driver capability diminishes, and perceived spare capacity (perceived driver capability minus perceived task demand) decreases. These patterns fluctuate under conditions of low perceived task demand or high perceived driver capability. When both conditions coincide, drivers necessitate a considerably longer time to regain vehicle control. Our findings on takeover time contribute to the development of strategies aimed at predicting drivers' takeover time, optimizing time budgets, fostering human-centred vehicle design, and enhancing the safety of conditionally automated driving.

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Current driver behaviour models (DBMs) are primarily designed for the general driver population under specific scenarios, such as car following or lane changing. Hence DBMs capturing individual behaviour under various scenarios are lacking. This paper presents a novel method to quantify individual perceived driving risk in the longitudinal and lateral directions using risk thresholds capturing the time headway and time to line crossing. These are integrated in a risk-based DBM formulated under a model predictive control (MPC) framework taking into account vehicle dynamics. The DBM assumes drivers to operate as predictive controllers jointly optimising multiple criteria, including driving risk, discomfort, and travel inefficiency. Simulation results in car following and passing a slower vehicle demonstrate that the DBM predicts plausible behaviour under representative driving scenarios, and that the risk thresholds are able to reflect individual driving behaviour. Furthermore, the proposed DBM is verified using empirical driving data collected from a driving simulator, and the results show it is able to accurately generate vehicle longitudinal and lateral control matching individual human drivers. Overall, this model can capture individual risk perception behaviour and can be applied to the design and assessment of intelligent vehicle systems.

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Automatische voertuigen bieden grote kansen op het gebied van toegankelijkheid, mobiliteit en verkeersveiligheid, als we de risico’s op tijd onder ogen zien, zegt Simeon Calvert.@en

Providing non-trivial certificates of safety for non-linear stochastic systems is an important open problem. One promising solution to address this problem is the use of barrier functions. Barrier functions are functions whose composition with the system forms a Martingale and enable the computation of the probability that the system stays within a safe set over a finite time horizon. However, existing approaches to find barrier functions generally restrict the search to a small class of functions, often leading to conservatism. To address this problem, in this letter, we parameterize barrier functions as neural networks and show that bound propagation techniques and linear programming can be successfully employed to find Neural Barrier Functions. Further, we develop a branch-and-bound scheme based on linear relaxations that improves the scalability of the proposed framework. On several case studies we show that our approach scales to neural networks of hundreds of neurons and multiple hidden layers and often produces certificates of safety that are tighter than state-of-the-art methods.@en

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Autonomous Vehicles (AVs). However, trajectories collected by AVs are noisy and not readily applicable for studying CF behaviour. This paper extracts and enhances two categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset. First, CF pairs are selected based on specific rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the raw CF data is corrected and enhanced via motion planning, Kalman filtering, and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following segments are obtained, with a total driving distance of 150k+ km. A diversity assessment shows that the processed data cover complete CF regimes for calibrating CF models. This open and ready-to-use dataset provides the opportunity to investigate the CF behaviours of following AVs vs. HVs from real-world data. It can further facilitate studies on exploring the impact of AVs on mixed urban traffic.@en

Intersections are critical bottlenecks within urban transportation networks. Current models for simulating two-dimensional (2D) vehicular movements at intersections are met with limitations in accurately representing complex interactions and capturing vehicle dynamics. Accordingly, this paper proposes a novel microsimulation framework for trajectory planning and vehicular control at intersections. The model considers vehicle dynamics and control variables, such as acceleration and steering angle, and releases the popular assumption that there is full knowledge sharing or cooperation among vehicles at intersections. These features make the proposed framework more realistic compared to previous microsimulation attempts and applicable to traffic flow and environmental impact assessment studies. In addition, it efficiently operates in realtime for multiple vehicles, overcoming the limitations of offline methods. Moreover, the model is capable of accounting for driver/vehicle detection range, reaction time, and perception and prediction inaccuracies, which enhances its suitability for safety assessments. The evaluation in several scenarios indicates the ability of the proposed framework in realtime planning and following realistic and consistent 2D paths while avoiding collisions with other vehicles.

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Ensuring operational control over automated vehicles is not trivial and failing to do so severely endangers the lives of road users. An integrated approach is necessary to ensure that all agents play their part including drivers, occupants, vehicle designers and governments. While progress is being made, a comprehensive approach to the problem is being ignored, which can be solved in the main through considering Meaningful Human Control (MHC). In this research, an Integrated System Proximity framework and Operational Process Design approach to assist the development of Connected Automated Vehicles (CAV) under the consideration of MHC are introduced. These offer a greater understanding and basis for vehicle and traffic system design by vehicle designers and governments as two important influencing stakeholders. The framework includes an extension to a system approach, which also considers ways that MHC can be improved through updating: either implicit proximal updating or explicit distal updating. The process and importance are demonstrated in three recent cases from practice. Finally, a call for action is made to government and regulatory authorities, as well as the automotive industry, to ensure that MHC processes are explicitly included in policy, regulations, and design processes to ensure future advancement of CAVs in a responsible, safe and humanly agreeable fashion.@en

Route guidance in traffic management aims to improve traffic network performance aligned with a system optimum. However, service providers commonly offer user optimum travel advice that can negatively impact centralized route guidance. This paper quantifies and demonstrates the impact of different policy strategies for a centralized route guidance systems where road authorities and service providers work together in a coordinated approach. Cooperation through an intermediary is considered with various policy strategies that consider different approaches and levels of cooperation between road authorities and service providers, which are evaluated using traffic modelling. A use case for the ring network of Milan shows that cooperation between the two parties has the potential to get the best out of the measure by utilizing a system optimum approach, while still allowing service providers to offer individual travel advice. The results of the modelled case study clearly show that the two approaches of far-reaching cooperation and increased compliance have a greater positive effect on traffic network performance in terms of reduced delays, reduced congestion and total time spent. In addition, the future presence of connected automated vehicles (CAV) is also considered in which these vehicle demonstrate full compliance. This shows that with increasing percentage of CAVs that route guidance can have a substantial positive effect compared to low compliance or a smaller penetration rate of automated vehicles.@en

The urban traffic environment is characterized by the presence of a highly differentiated pool of users, including vulnerable ones. This makes vehicle automation particularly difficult to implement, as a safe coordination among those users is hard to achieve in such an open scenario. Different strategies have been proposed to address these coordination issues, but all of them have been found to be costly for they negatively affect a range of human values (e.g. safety, democracy, accountability…). In this paper, we claim that the negative value impacts entailed by each of these strategies can be interpreted as lack of what we call Meaningful Human Control over different parts of a sociotechnical system. We argue that Meaningful Human Control theory provides the conceptual tools to reduce those unwanted consequences, and show how “designing for meaningful human control” constitutes a valid strategy to address coordination issues. Furthermore, we showcase a possible application of this framework in a highly dynamic urban scenario, aiming to safeguard important values such as safety, democracy, individual autonomy, and accountability. Our meaningful human control framework offers a perspective on coordination issues that allows to keep human actors in control while minimizing the active, operational role of the drivers. This approach makes ultimately possible to promote a safe and responsible transition to full automation.

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This study presents a new method to infer the average two-dimensional (2D) spacing between interacting vehicles in urban traffic from trajectory data. In this context, 2D spacing reflects the amount of road space consumed by pairs of interacting vehicles, and is related to 2D density at the macroscopic level. Due to complex interaction and conflicts in urban traffic, the inherent assumptions in traditional traffic flow models, such as unidirectional flow and homogeneity, are often violated. Such violation challenges direct measurement of urban vehicle spacing. The proposed method addresses this challenge by focusing on the relative movement between interacting vehicles and aggregating the accumulated presence of vehicles in similar scenarios. We apply the method to a large-scale urban trajectory dataset called pNEUMA, and validate the consistency of the method through bootstrapping. By applying the method we obtain a new empirical relation between the average 2D spacing and the relative speeds between interacting vehicles. There are similarities between this empirical relation with the classical Fundamental Diagram of traffic flow in terms of shape and interpretation, and so we term it the “interaction Fundamental Diagram” (iFD). However, there are also key differences. The iFD does not represent steady-state (homogeneous and stationary) longitudinal behaviour; it describes the average amount of road space needed for vehicle interactions at different relative speeds. We believe these iFD relations contribute to understanding vehicle interaction in urban traffic, and can offer new insights for designing safer and more efficient urban intersections.

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