Safe Autonomy Research Articles

Safe Autonomy: Sensor Fusion in Complex Traffic Environments

Advancements in autopilot driving and car technology have propelled the development of safe autonomy, particularly in navigating complex traffic environments. This paper delves into applying sensor fusion techniques in automobile systems, focusing on their relevance in ensuring safe navigation amidst the intricacies of Indian road conditions. Of course, the conditions of Indian roads have changed hugely and drastically over time but still, it would not be wrong to say that India is still grappling with a high incidence of road accidents exacerbated by non-compliance with traffic rules, integrating sensor fusion technologies becomes pivotal in enhancing the safety and reliability of autonomous systems. By including and adding data from various sensors such as cameras, LiDAR, and radar, these systems gain a comprehensive understanding of their surroundings, enabling real-time decision-making in dynamic traffic scenarios. Furthermore, the paper explores the integration of machine learning algorithms to augment sensor fusion capabilities, facilitating adaptive responses to erratic traffic behaviour and irregular driving practices. Through a technology-centric lens, this research aims to play the role of sensor fusion in bolstering the efficacy of autonomous systems on Indian roads, thereby contributing to the mitigation of road accidents and promoting safer transportation infrastructures.

Can Urban Air Mobility become reality? Opportunities and challenges of UAM as innovative mode of transport and DLR contribution to ongoing research

AbstractUrban Air Mobility (UAM) is a new air transportation system for passengers and cargo in urban environments, enabled by new technologies and integrated into multimodal transportation systems. The vision of UAM comprises the mass use in urban and suburban environments, complementing existing transportation systems and contributing to the decarbonization of the transport sector. Initial attempts to create a market for urban air transportation in the last century failed due to lack of profitability and community acceptance. Technological advances in numerous fields over the past few decades have led to a renewed interest in urban air transportation. UAM is expected to benefit users and to also have a positive impact on the economy by creating new markets and employment opportunities for manufacturing and operation of UAM vehicles and the construction of related ground infrastructure. However, there are also concerns about noise, safety and security, privacy and environmental impacts. Therefore, the UAM system needs to be designed carefully to become safe, affordable, accessible, environmentally friendly, economically viable and thus sustainable. This paper provides an overview of selected key research topics related to UAM and how the German Aerospace Center (DLR) contributed to this research in the project "HorizonUAM - Urban Air Mobility Research at the German Aerospace Center (DLR)". Selected research results on the topics of market potential and public acceptance, vehicle design (including battery degradation, onboard systems, cabin design, cabin simulation), infrastructure, operations (including U-space, safe autonomy, navigation, communication, cost modeling) and overall system modeling are briefly presented.

Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing

Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles’ practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as unpredictably switching to dangerous driving modes—putting its neighboring vehicles under risks; such undesired mode switching could arise from numbers of human driver factors, including fatigue, drunkenness, distraction, aggressiveness, and so on. On the other hand, modern vehicle-to-vehicle (V2V) communication technologies enable the autonomous vehicles to efficiently and reliably share the scarce run-time information with each other [ 1 ]. In this article, we propose, to the best of our knowledge, the first efficient algorithm that can (1) significantly improve trajectory prediction by effectively fusing the run-time information shared by surrounding autonomous vehicles, and can (2) accurately and quickly detect abnormal human driving mode switches or abnormal driving behavior with formal assurance without hurting human drivers’ privacy. To validate our proposed algorithm, we first evaluate our proposed trajectory predictor on NGSIM and Argoverse datasets and show that our proposed predictor outperforms the baseline methods. Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic. The best performance achieves detection rate of 97.3%, average detection delay of 1.2 s, and 0 false alarm.

Robotics, Artificial Intelligence, and Drones in Solar Photovoltaic Energy Applications—Safe Autonomy Perspective

While there is evidence of substantial improvement in efficiency and cost reduction from the integration of Robotics, Artificial Intelligence, and Drones (RAID) in solar installations; it is observed that there is limited oversight by international standards such as the International Electrotechnical Commission (IEC) in terms of the hazards and untapped potentials. This is partly because it is an emerging application and generally burdened with social acceptability issues. Thus, the safety regulations applied are adaptations of device-specific regulations as deemed fit by individual companies. Also, due to the fast-paced technological development of these platforms, there is huge potential for applications that are not currently supported by the device-specific regulations. This creates a multi-faceted demand for the establishment of standardized, industry-wide polices and guidelines on the use of RAID platforms for Solar PV integrations. This work aims to address critical safety concerns by conducting a comprehensive high-level system examination applicable to the monitoring and maintenance of Solar PV systems. Standard safety assurance models and approaches are examined to provide a safe autonomy perspective for Solar PVs. It is considered that, as RAID applications continue to evolve and become more prevalent in the Solar PV industry, standardized protocols or policies would be established to ensure safe and reliable operations.

Editorial: Special Issue on Opportunities and Challenges with Autonomous Racing

In the ever-evolving landscape of autonomous vehicles, competition and research of high-speed autonomous racing emerged as a captivating frontier, pushing the limits of perception, planning, and control. Autonomous racing presents a setup where the intersection of cutting-edge software and hardware development sparks unprecedented opportunities and confronts unique challenges. The motorsport axiom, “If everything seems under control, then you are not going fast enough,” resonates in this special issue, underscoring the demand for algorithms and hardware that can navigate at the cutting edge of control, traction, and agility. In pursuing autonomy at high speeds, the racing environment becomes a crucible, pushing autonomous vehicles to execute split-second decisions with high precision. Autonomous racing, we believe, offers a litmus test for the true capabilities of self-driving software. Just as racing has historically served as a proving ground for automotive technology, autonomous racing now presents itself as the crucible for testing self-driving algorithms. While routine driving situations dominate much of the autonomous vehicle operations, focusing on extreme situations and environments is crucial to support investigation into safety benefits. The urgency of advancing highspeed autonomy is palpable in burgeoning autonomous racing competitions like Formula Student Driverless, F1TENTH autonomous racing, Roborace, and the Indy Autonomous Challenge. These arenas provide a literal testbed for testing perception, planning, and control algorithms and symbolize the accelerating traction of autonomous racing as a proving ground for agile and safe autonomy. Our special issue focuses on cutting-edge research into software and hardware solutions for highspeed autonomous racing. We sought contributions from the robotics and autonomy communities that delve into the intricacies of head-to-head multi-agent racing: modeling vehicle dynamics at high speeds, developing advanced perception, planning, and control algorithms, as well as the demonstration of algorithms, in simulation and in real-world vehicles. While presenting recent developments for autonomous racing, we believe these special issue papers will also create an impact in the broader realm of autonomous vehicles.

Towards Safe and Resilient Autonomy in Multi-Robot Systems

In the near future, autonomous systems such as multi-robot systems are envisioned to increasingly co-exist with hu- mans in our daily lives, from household service to large- scale warehouse logistics, agriculture environment sampling, and smart city. In these applications, robots and humans as networked heterogeneous components will frequently inter- act with each other in a variety of scenarios under uncer- tain, rapidly-changing, and possibly hostile environment. On one hand, harmonious interactions among robots, as well as between robots and humans, would require safe integration (e.g. collision-free close-proximity interactions) of heteroge- neous robots, human, and human-robot autonomy. On the other hand, reliable interactions among autonomous multi- robot systems often call for resilient system integrity (e.g. communication capability with potential robot failures) to re- tain its capability of accomplishing complex tasks through coordinated behaviors. In the proposed talk, I will discuss our recent works towards safe autonomy and resilient autonomy that aim to facilitate correct-by-design robotic behaviors in a variety of applications.

자치경찰을 활용한 안전자치 실현방안

The implementation of the autonomous police system means not only changes in police work, but also new expectations for the role of the police. The implementation of the new strategy of the autonomous police system is in its infancy, and there are many reports of poor awareness of the citizens about the change in the system. The most important thing is that the side effects that occur in the change of the system directly affect the citizens. In the case of safe self-government, 'voluntary' activities are emphasized first in the scope of the civic life zone, centering on 'local safety'. Above all, in order to realize safe self-government, it is important to form a safety culture based on citizens' voluntary initiative. This study focuses on finding ways to realize safe autonomy through the implementation of the autonomous police system. The development plan was derived as a research method that analyzes literature and official data. In order to realize safe autonomy, it is important to analyze local characteristics to discover risk factors and to engage in cooperative and voluntary activities. First, a local safety map centered on local residents should be prepared. Second, it is necessary to set priorities and continuously manage local safety evaluation indicators. Third, it is necessary to improve the recognition and sharing of local citizens' requests. Fourth, communication channels should be strengthened in relation to safety information.

Towards Safe Autonomy in Assistive Robots

Towards Safe Autonomy in Assistive Robots

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities.

GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning With End-to-End Learning

We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. In particular, our synthesized policies only require relative state information of nearby neighbors and obstacles, and compute a provably-safe action. Our approach has three major components: i) we generate demonstration trajectories using a global planner and extract local observations from them, ii) we use deep imitation learning to learn a decentralized policy that can run efficiently online, and iii) we introduce a novel differentiable safety module to ensure collision-free operation, thereby allowing for end-to-end policy training. Our numerical experiments demonstrate that our policies have a 20% higher success rate than optimal reciprocal collision avoidance, ORCA, across a wide range of robot and obstacle densities. We demonstrate our method on an aerial swarm, executing the policy on low-end microcontrollers in real-time.

Design for All: A proposal for inclusion and safe autonomy

Design for All: A proposal for inclusion and safe autonomy

Towards an Open Software Platform for Field Robots in Precision Agriculture

Robotics in precision agriculture has the potential to improve competitiveness and increase sustainability compared to current crop production methods and has become an increasingly active area of research. Tractor guidance systems for supervised navigation and implement control have reached the market, and prototypes of field robots performing precision agriculture tasks without human intervention also exist. But research in advanced cognitive perception and behaviour that is required to enable a more efficient, reliable and safe autonomy becomes increasingly demanding due to the growing software complexity. A lack of collaboration between research groups contributes to the problem. Scientific publications describe methods and results from the work, but little field robot software is released and documented for others to use. We hypothesize that a common open software platform tailored to field robots in precision agriculture will significantly decrease development time and resources required to perform experiments due to efficient reuse of existing work across projects and robot platforms. In this work we present the FroboMind software platform and evaluate the performance when applied to precision agriculture tasks.

Autonomy and insulin in adolescents with type 1 diabetes

Research concerning adolescent development and diabetes that considers the impact of various new treatment modalities has valuable implications for the development of appropriate education and intervention strategies for this population. The recommendation for intensive insulin therapy in type 1 diabetes (T1D) in adolescence has resulted in a number of studies demonstrating the medical benefits of the various modalities of such treatment (1–3). However, fewer studies explore the psychosocial impact of this intensive therapy on adolescents and families (4). In this article, we examine the research on autonomy in adolescents with T1D and consider the ‘pump vs. injections’ debate from a developmental perspective. The final section of this article includes general recommendations and pump-specific considerations for diabetes health-care providers to encourage safe autonomy development in adolescents with T1D. T1D is a genetic disease of the immune system characterized by pancreatic beta cell destruction and a consequent lack of insulin. Recent data suggest that 15 000 youth in the USA are diagnosed with T1D each year, and the incidence rate is highest in children aged 10–14 (5). The recommendations by the American Diabetes Association (ADA) support the use of an intensive insulin regimen that involves a ‘basal/bolus’ pattern of insulin administration to maintain blood glucose levels as close to normal as possible, with a target hemoglobin A1c (HbA1c) of less than 7.5% for adolescents (aged 13–19) (6). This pattern can be achieved by either multiple daily injections (MDI), which involve a daily injection of a long-acting (basal) insulin and injections of rapid-acting (bolus) insulin to cover meals and to correct high-blood sugars, or continuous subcutaneous insulin infusion (CSII), in which basal insulin is delivered by the constant infusion of small amounts of rapid-acting insulin through an insulin pump (6–8). Both types of basal/bolus therapy require frequent blood glucose monitoring (at least four/day) and tracking carbohydrate intake and exercise in order to make insulin dosing decisions (9). The ADA also emphasizes the need for developmentally appropriate interventions that include adolescents and their caregivers (6).