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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. 

The supply chains of semiconductors and integrated devices supports industry across all economic sectors. Globally, the supply chain is experiencing a variety of stressors and disruptions, with effects that cascade across the economy, causing product delays and enterprise losses. However, quantitative models that support an understanding of how stressors influence supply chain performance are needed. Here we show how stress testing can be used for assessing the impact of disruptions on supply chain performance metrics and for characterizing system resilience. We demonstrate a framework that utilizes discrete event simulation for stress testing the resilience of a semiconductor supply chain. Our results include a comparison of resilience curves with and without risk management countermeasures, showing the resilience-enhancing benefits of various supply chain management strategies such as maintaining safety stock and sourcing from multiple suppliers. Supply chain managers can utilize stress testing principles and methodologies to configure their supply chain and engage in practices that contribute to system resilience. 

Low-lying coastal cities across the world are increasingly seeing flooding due to climate change and accompanying sea-level rise. Many such cities rely on old and passive stormwater infrastructure which cannot cope up with the increasing flood risk. One potential solution for addressing coastal flooding is implementing active control strategies in stormwater systems. Active stormwater control relies on rule-based strategies, which is not able to manage the increasing flood risk. Model predictive control (MPC) for stormwater flood management is getting attention over the past decade. However, building physics-based models for MPC in stormwater management is cost and time prohibitive. In this paper, we develop a data-driven approach, which utilizes unstructured state-space models for system identification and predictive control implementation. We demonstrate our results using two real stormwater network configurations, one from the Norfolk, VA region and another model of Ann Arbor region, MI, respectively. Our results indicate that MPC outperforms rule-based strategies by up to 60% of the Norfolk model and up to 90% of the Ann Arbor model in flood management. 

This paper presents an adaptive lookahead pure-pursuit lateral controller for optimizing racing metrics such as lap time, average lap speed, and deviation from a reference trajectory in an autonomous racing scenario. We propose a greedy algorithm to compute and assign optimal lookahead distances for the pure-pursuit controller for each waypoint on a reference trajectory for improving the race metrics. We use a ROS based autonomous racing simulator to evaluate the adaptive pure-pursuit algorithm and compare our method with several other pure-pursuit based lateral controllers. We also demonstrate our approach on a scaled real testbed using a F1/10 autonomous racecar. Our method results in a significant improvement (20%) in the racing metrics for an autonomous racecar. 

The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approaches that are used in the fields of perception, planning and control as well as end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to a range of high performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms emphasizing both the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field we conclude with a summary of open research challenges that will guide future researchers in this field. 

Multi-agent autonomous racing is a challenging problem for autonomous vehicles due to the split-second, and complex decisions that vehicles must continuously make during a race. The presence of other agents on the track requires continuous monitoring of the ego vehicle’s surroundings, and necessitates predicting the behavior of other vehicles so the ego can quickly react to a changing environment with informed decisions. In our previous work we have developed the DeepRacing AI framework for autonomous formula one racing. Our DeepRacing framework was the first implementation to use the highly photorealisitc Formula One game as a simulation testbed for autonomous racing. We have successfully demonstrated single agent high speed autonomous racing using Bezier curve trajectories. In this paper, we extend the capabilities of the DeepRacing framework towards multi-agent autonomous racing. To do so, we first develop and learn a virtual camera model from game data that the user can configure to emulate the presence of a camera sensor on the vehicle. Next we propose and train a deep recurrent neural network that can predict the future poses of opponent agents in the field of view of the virtual camera using vehicles position, velocity, and heading data with respect to the ego vehicle racecar. We demonstrate early promising results for both these contributions in the game. These added features will extend the DeepRacing framework to become more suitable for multi-agent autonomous racing algorithm development 

A popular metric for measuring progress in autonomous driving has been the "miles per intervention". This is nowhere near a sufficient metric and it does not allow for a fair comparison between the capabilities of two autonomous vehicles (AVs). In this paper we propose Scenario2Vector - a Scenario Description Language (SDL) based embedding for traffic situations that allows us to automatically search for similar traffic situations from large AV data-sets. Our SDL embedding distills a traffic situation experienced by an AV into its canonical components - actors, actions, and the traffic scene. We can then use this embedding to evaluate similarity of different traffic situations in vector space. We have also created a first of its kind, Traffic Scenario Similarity (TSS) dataset which contains human ranking annotations for the similarity between traffic scenarios. Using the TSS data, we compare our SDL embedding -with textual caption based search methods such as Sentence2Vector. We find that Scenario2Vector outperforms Sentence2Vector by 13% ; and is a promising step towards enabling fair comparisons among AVs by inspecting how they perform in similar traffic situations. We hope that Scenario2Vector can have a similar impact to the AV community that Word2Vec/Sent2Vec have had in Natural Language Processing datasets.