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High throughput sequencing technologies have become essential in the fields of evolutionary biology and genomics. When dealing with non model organisms or genomic gigantism, sequencing whole genomes is still relatively costly and therefore reduced-genome representations are frequently obtained, for instance by \"target capture\" approaches. While computational tools exist that can handle target capture data and identify small scale variants such as single nucleotide polymorphisms and micro-indels, options to identify large scale structural variants are limited. To meet this need, we introduce PAVspotter: a tool that can identify presence/absence variation (PAV) in target capture data. PAVspotter conducts a signal cross-correlation calculation, in which the distribution of read counts per target between samples of different a priori defined classes, e.g. male versus female, or diseased versus healthy, are compared. We apply and test our methodology by studying Triturus newts: salamanders with gigantic genomes that currently lack an annotated reference genome. Triturus newts suffer from a hereditary disease that kills half their offspring during embryogenesis. We compare the target capture data of two different types of diseased embryos, characterized by unique deletions, with those of healthy embryos. Our findings show that PAVspotter helps to expose such structural variants, even in the face of medium to low sequencing coverage levels, low sample sizes, and background noise due to mismapped reads. PAVspotter can be used to study the structural variation underlying supergene systems in the absence of chromosome level genome assemblies. The code, including further explanation on how to use and customize it, is available through the PAVspotter GitHub repository.Competing Interest StatementThe authors have declared no competing interest.Footnotes* The only thing, content-wise, that has changed is the Funding section. We added a relevant grant to the ones that were already mentioned for the sake of completeness.* https://github.com/Wielstra-Lab/PAVspotter* https://www.ncbi.nlm.nih.gov/sra/PRJNA1111729* https://zenodo.org/records/13991751

With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. Once a fault or failure is detected, there is a need to classify its severity and decide on appropriate and safe mitigating actions. To provide a solution to this mitigation challenge, in this paper a functional-safety architecture is proposed and an optimization-based mitigation algorithm is introduced. This algorithm uses nonlinear model predictive control (NMPC) to bring a vehicle, suffering from a severe fault, such as a power steering failure, to a safe-state. The internal model of the NMPC uses the information from the fault detection, isolation and identification to optimize the tracking performance of the controller, showcasing the need of the proposed architecture. Given a string of ACC vehicles, our results demonstrate a variety of tactical decision-making approaches that a fault-affected vehicle could employ to manage any faults. Furthermore, we show the potential for improving the safety of the affected vehicle as well as the effect of these approaches on the duration of the manoeuvre.

The urban environment is amongst the most difficult domains for autonomous vehicles. The vehicle must be able to plan a safe route on challenging road layouts, in the presence of various dynamic traffic participants such as vehicles, cyclists and pedestrians and in various environmental conditions. The challenge remains to have motion planners that are computationally fast and that account for future movements of other road users proactively. This paper describes an computationally efficient sampling-based trajectory planner for safe and comfortable driving in urban environments. The planner improves the Stable-Sparse-RRT algorithm by adding initial exploration branches to the search tree based on road layout information and reiterating the previous solution. Furthermore, the trajectory planner accounts for the predicted motion of other traffic participants to allow for safe driving in urban traffic. Simulation studies show that the planner is capable of planning collision-free, comfortable trajectories in several urban traffic scenarios. Adding the domain-knowledge-based exploration branches increases the efficiency of exploration of highly interesting areas, thereby increasing the overall planning performance.

Developing safe automated vehicles that can be proactive, safe, and comfortable in mixed traffic requires improved planning methods that are risk-averse and that account for predictions of the motion of other road users. To consider these criteria, in this article, we propose a non-linear model-predictive trajectory generator scheme, which couples the longitudinal and lateral motion of the vehicle to steer the vehicle with minimal risk, while progressing towards the goal state. The proposed method takes into account the infrastructure, surrounding objects, and predictions of the objects' state through artificial potential-based risk fields included in the cost function of the model-predictive control (MPC) problem. This trajectory generator enables anticipatory maneuvers, i.e., mitigating risk far before any safety-critical intervention would be necessary. The method is proven in several case studies representing both highways- and urban situations. The results show the safe and efficient implementation of the MPC trajectory generator while proving its real-time applicability.

As vehicle automation advances, motion planning algorithms face escalating challenges in achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems (ADAS) primarily focus on basic tasks, leaving unexpected scenarios for human intervention, which can be error-prone. Motion planning approaches for higher levels of automation in the state-of-the-art are primarily oriented toward the use of risk- or anti-collision constraints, using over-approximates of the shapes and sizes of other road users to prevent collisions. These methods however suffer from conservative behavior and the risk of infeasibility in high-risk initial conditions. In contrast, our work introduces a novel multi-objective trajectory generation approach. We propose an innovative method for constructing risk fields that accommodates diverse entity shapes and sizes, which allows us to also account for the presence of potentially occluded objects. This methodology is integrated into an occlusion-aware trajectory generator, enabling dynamic and safe maneuvering through intricate environments while anticipating (potentially hidden) road users and traveling along the infrastructure toward a specific goal. Through theoretical underpinnings and simulations, we validate the effectiveness of our approach. This paper bridges crucial gaps in motion planning for automated vehicles, offering a pathway toward safer and more adaptable autonomous navigation in complex urban contexts.

Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the time horizon associated with these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been conducted on how varying prediction lengths affect AV safety and other vehicle performance metrics, resulting in undefined horizon requirements for prediction methods. Our study addresses this gap by examining the effects of different prediction horizons on AV performance, focusing on safety, comfort, and efficiency. Through multiple experiments using a state-of-the-art, risk-based predictive trajectory planner, we simulated predictions with horizons up to 20 seconds. Based on our simulations, we propose a framework for specifying the minimum required and optimal prediction horizons based on specific AV performance criteria and application needs. Our results indicate that a horizon of 1.6 seconds is required to prevent collisions with crossing pedestrians, horizons of 7-8 seconds yield the best efficiency, and horizons up to 15 seconds improve passenger comfort. We conclude that prediction horizon requirements are application-dependent, and recommend aiming for a prediction horizon of 11.8 seconds as a general guideline for applications involving crossing pedestrians.

In a balanced lethal system, half the reproductive output succumbs. Triturus newts are the best-known example. Their chromosome 1 comes in two distinct versions and embryos carrying the same version twice experience developmental arrest. Those possessing two different versions survive, suggesting that each version carries something uniquely vital. With target capture we obtain over 7,000 nuclear DNA markers across the genus Triturus and all main lineages of Salamandridae (the family to which Triturus belongs) to investigate the evolutionary history of Triturus chromosome 1 versus other chromosomes. Dozens of genes are completely missing from either one or the other version of chromosome 1 in Triturus. Furthermore, the unique gene content of 1A versus 1B is remarkably similar across Triturus species, suggesting that the balanced lethal system evolved before Triturus radiated. The tree topology of chromosome 1 differs from the rest of the genome, presumably due to pervasive, ancient hybridization between Triturus ancestor and other newt lineages. Our findings accentuate the complex nature of Triturus chromosome 1, the \"bloopergene\" driving the evolutionarily enigmatic balanced lethal system.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Only the Data Availability section has been slightly reworded and one link has been added (the Oxford Nanopore Technology read data link - which has also been added under \"Manuscript Basics\" URLs here in the system). Content-wise, nothing about the manuscript changed.* https://github.com/Wielstra-Lab/Triturus_chr1_bloopergenes* https://zenodo.org/records/13991240* https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA1171613* https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA498336* https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA1173497* https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1216568

The introduction of highly automated vehicles on the public road may improve safety and comfort, although its success will depend on social acceptance. This requires trajectory planning methods that provide safe, proactive, and comfortable trajectories that are risk-averse, take into account predictions of other road users, and comply with traffic rules, social norms, and contextual information. To consider these criteria, in this article, we propose a non-linear model-predictive trajectory generator. The problem space is populated with risk fields. These fields are constructed using a novel application of a knowledge graph, which uses a traffic-oriented ontology to reason about the risk of objects and infrastructural elements, depending on their position, relative velocity, and classification, as well as depending on the implicit context, driven by, e.g., social norms or traffic rules. Through this novel combination, an adaptive trajectory generator is formulated which is validated in simulation through 4 use cases and 309 variations and is shown to comply with the relevant social norms, while taking minimal risk and progressing towards a goal area.

Autonomous emergency steering (AES) systems have the promising potential to further reduce traffic fatalities with other (potentially vulnerable) traffic participants by using relatively small lateral deviations to realize collision-free behavior. In this work, a complete and tractable software architecture is presented for such an AES system, comprising of the estimation of the vehicles capabilities, planning a set of paths which exploit these capabilities, checking the feasibility and risk of these paths and eventually triggering the decision to drive along one of these paths, i.e., initiating an AES manoeuvre. A novel methodology is provided to trigger such an AES system, which is based on a time-to-evade (TTE) notion. In the presence of time-varying uncertainties or measurement inaccuracies, the system is able to replan the path from the previously chosen path to ensure collision-free behavior. The proposed architecture and control approach is validated using a simulation study and field tests, showing the effectiveness of the architecture and its sub-components.

In this article, we propose a tractable nonlinear fault isolation filter along with explicit performance bounds for a class of nonlinear dynamical systems. We consider the presence of additive and multiplicative faults, occurring simultaneously and through an identical dynamical relationship, which represents a relevant case in several application domains. The proposed filter architecture combines tools from model-based approaches in the control literature and regression techniques from machine learning. To this end, we view the regression operator through a system-theoretic perspective to develop operator bounds that are then utilized to derive performance bounds for the proposed estimation filter. In the case of constant, simultaneously and identically acting additive and multiplicative faults, it can be shown that the estimation error converges to zero with an exponential rate. The performance of the proposed estimation filter in the presence of incipient faults is validated through an application on the lateral safety systems of SAE level 4 automated vehicles. The numerical results show that the theoretical bounds of this study are indeed close to the actual estimation error.