SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving

Autonomous driving confronts great challenges in complex traffic scenarios, where the risk of Safety of the Intended Functionality (SOTIF) can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles (AVs), but also inherently in the performance limitation risk of the implemented algorithms themselves. How to minimize the SOTIF risk for autonomous driving is currently a critical, difficult, and unresolved issue. Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks. The core of this system is the risk monitoring of the implemented artificial intelligence algorithms within the AV. As a demonstration of the Self-Surveillance and Self-Adaption System, the risk monitoring of the perception algorithm, i.e., YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and external collision risk are jointly quantified via SOTIF entropy, which is then propagated downstream to the decision-making module and mitigated. Finally, several challenging scenarios are demonstrated, and the Hardware-in-the-Loop experiments are conducted to verify the efficiency and effectiveness of the system. The results demonstrate that the Self-Surveillance and Self-Adaption System enables dependable online monitoring, quantification, and mitigation of SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT

Evidence for the contribution of COMT gene Val158/108Met polymorphism (rs4680) to working memory training-related prefrontal plasticity.

BackgroundGenetic factors have been suggested to affect the efficacy of working memory training. However, few studies have attempted to identify the relevant genes.MethodsIn this study, we first performed a randomized controlled trial (RCT) to identify brain regions that were specifically affected by working memory training. Sixty undergraduate students were randomly assigned to either the adaptive training group (N = 30) or the active control group (N = 30). Both groups were trained for 20 sessions during 4 weeks and received fMRI scans before and after the training. Afterward, we combined the data from the 30 participants in the RCT study who received adaptive training with data from 71 additional participants who also received the same adaptive training but were not part of the RCT study (total N = 101) to test the contribution of the COMT Val158/108Met polymorphism to the interindividual difference in the training effect within the identified brain regions.ResultsIn the RCT study, we found that the adaptive training significantly decreased brain activation in the left prefrontal cortex (TFCE-FWE corrected p = .030). In the genetic study, we found that compared with the Val allele homozygotes, the Met allele carriers' brain activation decreased more after the training at the left prefrontal cortex (TFCE-FWE corrected p = .025).ConclusionsThis study provided evidence for the neural effect of a visual-spatial span training and suggested that genetic factors such as the COMT Val158/108Met polymorphism may have to be considered in future studies of such training

Effect of rs1344706 in the ZNF804A gene on the brain network.

ZNF804A rs1344706 (A/C) was the first SNP that reached genome-wide significance for schizophrenia. Recent studies have linked rs1344706 to functional connectivity among specific brain regions. However, no study thus far has examined the role of this SNP in the entire functional connectome. In this study, we used degree centrality to test the role of rs1344706 in the whole-brain voxel-wise functional connectome during the resting state. 52 schizophrenia patients and 128 healthy controls were included in the final analysis. In our whole-brain analysis, we found a significant interaction effect of genotype × diagnosis at the precuneus (PCU) (cluster size = 52 voxels, peak voxel MNI coordinates: x = 9, y = - 69, z = 63, F = 32.57, FWE corrected P < 0.001). When we subdivided the degree centrality network according to anatomical distance, the whole-brain analysis also found a significant interaction effect of genotype × diagnosis at the PCU with the same peak in the short-range degree centrality network (cluster size = 72 voxels, F = 37.29, FWE corrected P < 0.001). No significant result was found in the long-range degree centrality network. Our results elucidated the contribution of rs1344706 to functional connectivity within the brain network, and may have important implications for our understanding of this risk gene's role in functional dysconnectivity in schizophrenia

The impact of novel coronavirus SARS-CoV-2 among healthcare workers in hospitals: An aerial overview

The ongoing outbreak of COVID-19, caused by the novel coronavirus SARS-CoV-2, places healthcare workers at an increased risk of infection as they are in close contact with patients. In this article, we report an overview of cases of infected healthcare workers in China and Italy during the early periods of the COVID-19 epidemic. China’s coronavirus response highlights the importance of implementing effective public health strategies. The authorities worldwide therefore, need to be extremely cautious when they implement stringent protective measures that safeguard healthcare workers in hospitals and counteract the threats created by the pandemic. Key Words: COVID-19 disease, Medical staff, Protective measures, Severe acute respiratory syndrome, coronavirus 2, Person-to-person transmissio

Modeling and Planning for Connected, Cooperative, and Automated Mobility Systems

Networks of connected, cooperative, and automated vehicles will operate future mobility systems. Such mobility systems will reshape our current transportation systems to be safer, more efficient, and more affordable. They will help us eliminate fatal collisions, improve traffic throughput and energy efficiency, and make traveling cheaper with a shared-trip service. Connectivity and automation technologies are not yet reaching their full potential. Nowadays, many vehicles are connected, but mainly for infotainment, navigation, and maintenance purposes. In the meantime, they are also increasingly automated by using measurements from onboard sensors. However, these connected-automated vehicles (CAVs) are not yet cooperative because their actions are not coordinated based on shared information among each other. Apart from cooperation among each other, CAVs of a cooperative, connected, and automated mobility system must also interact with human traffic participants efficiently and safely. The main challenge is how to anticipate human factors, such as human routing and driving behaviors. Most existing approaches to vehicle coordination overlook or over-simplify the behaviors of other human traffic participants. This dissertation studies the modeling and planning of cooperative, connected, and automated mobility systems. Modeling refers to the generation of models with predictive abilities for intelligent agents to understand the world where they are situated. At the same time, planning refers to optimal policies for intelligent agents through coordination and communication to improve traffic efficiency, safety, and mobility. Three applications are studied throughout the dissertation, covering the essential tasks of connected, cooperative, and automated mobility systems. Firstly, a ride-sharing problem was solved among a shared CAV fleet to determine an optimal dispatching strategy. We proposed a decentralized ride-sharing algorithm using multi-agent reinforcement learning (RL). To better represent vehicles and requests with changing quantities and attributes, we formulated them as graphs and implemented graph neural networks to learn their relational information. We adopted a hierarchical framework to unify pick-up, drop-off, and rebalance tasks and distributed decision-making and computation among vehicles and servers. This dissertation shows that the RL-based decentralized method can achieve dispatching performance on par with the centralized optimization-based method and better computation efficiency. Next, the CAVs inside a bounded urban area determine their routes given origins and destinations. We studied the energy-efficient routing of plug-in hybrid vehicles. We developed an energy consumption model that could capture plug-in hybrid vehicles' distinct energy consumption patterns. An energy-efficient routing algorithm was proposed to minimize energy consumption. The proposed algorithm included travel time constraints to consider the balance between saving travel time and energy. It can simultaneously find the optimal path and powertrain switching strategy. We also studied the cooperation among vehicles, proposed a cooperative routing algorithm based on decentralized Monte Carlo tree search, and showed that the proposed cooperative routing algorithm could save travel time and energy. Finally, the CAVs predict the motion of other human-driven vehicles and plan their future trajectories accordingly in a roundabout scenario. We proposed a multi-modal trajectory prediction algorithm that could separately consider the uncertainty from routing intentions and maneuvers using graph transformer networks. With prediction in Frenet space, the proposed algorithm can predict distinct future trajectories. We implemented a deterministic sampling-based trajectory planning algorithm for planning and integrated it with the proposed prediction algorithm. This dissertation shows that the proposed prediction and planning integration could achieve a safer and smoother interaction with other CAVs and human-driven vehicles.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/176499/1/boqili_1.pd

WAT: Improve the Worst-Class Robustness in Adversarial Training

Deep Neural Networks (DNN) have been shown to be vulnerable to adversarial examples. Adversarial training (AT) is a popular and effective strategy to defend against adversarial attacks. Recent works have shown that a robust model well-trained by AT exhibits a remarkable robustness disparity among classes, and propose various methods to obtain consistent robust accuracy across classes. Unfortunately, these methods sacrifice a good deal of the average robust accuracy. Accordingly, this paper proposes a novel framework of worst-class adversarial training and leverages no-regret dynamics to solve this problem. Our goal is to obtain a classifier with great performance on worst-class and sacrifice just a little average robust accuracy at the same time. We then rigorously analyze the theoretical properties of our proposed algorithm, and the generalization error bound in terms of the worst-class robust risk. Furthermore, we propose a measurement to evaluate the proposed method in terms of both the average and worst-class accuracies. Experiments on various datasets and networks show that our proposed method outperforms the state-of-the-art approaches

The roles of migrasome in development

Migrasomes are newly identified vesicular structures that mainly come from the ends and crosspoints of retracting fibers in moving cells. Their creation is closely linked with cell movement and goes through three key steps: Nucleation, Maturation, and Expansion. They eventually get released in an event called migracytosis. Migrasomes have become an interesting focus in cell communication, especially during processes like development. They transport a mix of chemokines, growth factors, and morphogens. Their study can offer fresh perspectives on developmental gradients and improve our understanding of how development works. In the mini-review, we summarize our recent progress on the role of migrasomes in development, with a special focus on how migrasomes contribute to the spatial distribution of signalling molecules