Download or read book Safe and Scalable Planning Under Uncertainty for Autonomous Driving written by Maxime Thomas Marcel Bouton and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving has the potential to significantly improve safety. Although progress has been made in recent years to deploy automated driving technologies, many situations handled on a daily basis by human drivers remain challenging for autonomous vehicles, such as navigating urban environments. They must reach their goal safely and efficiently while considering a multitude of traffic participants with rapidly changing behavior. Hand-engineering strategies to navigate such environments requires anticipating many possible situations and finding a suitable behavior for each, which places a large burden on the designer and is unlikely to scale to complicated situations. In addition, autonomous vehicles rely on on-board perception systems that give noisy estimates of the location and velocity of others on the road and are sensitive to occlusions. Autonomously navigating urban environments requires algorithms that reason about interactions with and between traffic participants with limited information. This thesis addresses the problem of automatically generating decision making strategies for autonomous vehicles in urban environments. Previous approaches relied on planning with respect to a mathematical model of the environment but have many limitations. A partially observable Markov decision process (POMDP) is a standard model for sequential decision making problems in dynamic, uncertain environments with imperfect sensor measurements. This thesis demonstrates a generic representation of driving scenarios as POMDPs, considering sensor occlusions and interactions between road users. A key contribution of this thesis is a methodology to scale POMDP approaches to complex environments involving a large number of traffic participants. To reduce the computational cost of considering multiple traffic participants, a decomposition method leveraging the strategies of interacting with a subset of road users is introduced. Decomposition methods can approximate the solutions to large sequential decision making problems at the expense of sacrificing optimality. This thesis introduces a new algorithm that uses deep reinforcement learning to bridge the gap with the optimal solution. Establishing trust in the generated decision strategies is also necessary for the deployment of autonomous vehicles. Methods to constrain a policy trained using reinforcement learning are introduced and combined with the proposed decomposition techniques. This method allows to learn policies with safety constraints. To address state uncertainty, a new methodology for computing probabilistic safety guarantees in partially observable domains is introduced. It is shown that the new method is more flexible and more scalable than previous work. The algorithmic contributions present in this thesis are applied to a variety of driving scenarios. Each algorithm is evaluated in simulation and compared to previous work. It is shown that the POMDP formulation in combination with scalable solving methods provide a flexible framework for planning under uncertainty for autonomous driving.

Download or read book Belief State Planning for Autonomous Driving Planning with Interaction Uncertain Prediction and Uncertain Perception written by Hubmann, Constantin and published by KIT Scientific Publishing. This book was released on 2021-09-13 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.

Download or read book Safety and Efficiency in Autonomous Vehicles Through Planning with Uncertainty written by Zachary Nolan Sunberg and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Safety is the highest priority for autonomous vehicles, but if they are not also efficient in terms of time and other resources, they will have a significant competitive disadvantage and may not be adopted widely. Though safety and efficiency are opposing goals, better models and planning algorithms can result in simultaneous improvements to both. The partially observable Markov decision process (POMDP) provides a systematic framework for representing the chain of decisions that an autonomous vehicle makes when driving or flying. However, it is challenging to find optimal policies for POMDPs that represent continuous physical domains. This dissertation analyzes and demonstrates improvements related to several aspects of making safe and efficient decisions. First, it considers how pseudo-random approximate algorithms can be combined with trusted deterministic algorithms to make certification easier and increase reliability in an unmanned aerial vehicle domain. Second, simulation results demonstrate that modeling uncertainty in the internal states of other road users using POMDP planning can lead to significant improvement over a formulation that models only outcome uncertainty. Third, the research shows that current leading online POMDP algorithms are unable to solve some problems with continuous observation spaces and overcomes this weakness using double progressive widening and weighted particle filtering resulting in a new algorithm called POMCPOW. Finally, a description of the POMDPs.jl software framework is given.

Download or read book Interaction aware Planning Under Uncertainty for Autonomous Driving written by Salar Arbabi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note is part of Quality testing.

Download or read book Probabilistic Motion Planning for Automated Vehicles written by Naumann, Maximilian and published by KIT Scientific Publishing. This book was released on 2021-02-25 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.

Download or read book Motion Planning for Autonomous Vehicles in Partially Observable Environments written by Taş, Ömer Şahin and published by KIT Scientific Publishing. This book was released on 2023-10-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.

Download or read book Safe Interactive Motion Planning for Autonomous Cars written by Mingyu Wang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, the autonomous driving industry has seen tremendous advancements thanks to the progress in computation, artificial intelligence, sensing capabilities, and other technologies related to autonomous vehicles. Today, autonomous cars operate in dense urban traffic, compared to the last generation of robots that were confined to isolated workspaces. In these human-populated environments, autonomous cars need to understand their surroundings and behave in an interpretable, human-like manner. In addition, autonomous robots are engaged in more social interactions with other humans, which requires an understanding of how multiple reactive agents act. For example, during lane changes, most attentive drivers would slow down to give space if an adjacent car shows signs of executing a lane change. For an autonomous car, understanding the mutual dependence between its action and others' actions is essential for the safety and viability of the autonomous driving industry. However, most existing trajectory planning approaches ignore the coupling between all agents' behaviors and treat the decisions of other agents as immutable. As a result, the planned trajectories are conservative, less intuitive, and may lead to unsafe behaviors. To address these challenges, we present motion planning frameworks that maintain the coupling of prediction and planning by explicitly modeling their mutual dependency. In the first part, we examine reciprocal collision avoidance behaviors among a group of intelligent robots. We propose a distributed, real-time collision avoidance algorithm based on Voronoi diagrams that only requires relative position measurements from onboard sensors. When necessary, the proposed controller minimally modifies a nominal control input and provides collision avoidance behaviors even with noisy sensor measurements. In the second part, we introduce a nonlinear receding horizon game-theoretic planner that approximates a Nash equilibrium in competitive scenarios among multiple cars. The proposed planner uses a sensitivity-enhanced objective function and iteratively plans for the ego vehicle and the other vehicles to reach an equilibrium strategy. The resulting trajectories show that the ego vehicle can leverage its influence on other vehicles' decisions and intentionally change their courses. The resulting trajectories exhibit rich interactive behaviors, such as blocking and overtaking in competitive scenarios among multiple cars. In the last part, we propose a risk-aware game-theoretic planner that takes into account uncertainties of the future trajectories. We propose an iterative dynamic programming algorithm to solve a feedback equilibrium strategy set for interacting agents with different risk sensitivities. Through simulations, we show that risk-aware planners generate safer behaviors when facing uncertainties in safety-critical situations. We also present a solution for the "inverse" risk-sensitive planning algorithm. The goal of the inverse problem is to learn the cost function as well as risk sensitivity for each individual. The proposed algorithm learns the cost function parameters from datasets collected from demonstrations with various risk sensitivity. Using the learned cost function, the ego vehicle can estimate the risk profile of an interacting agent online to improve safety and efficiency.

Download or read book Trajectory Planning of an Autonomous Vehicle in Multi Vehicle Traffic Scenarios written by Mahdi Morsali and published by Linköping University Electronic Press. This book was released on 2021-03-25 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tremendous industrial and academic progress and investments have been made in au-tonomous driving, but still many aspects are unknown and require further investigation,development and testing. A key part of an autonomous driving system is an efficient plan-ning algorithm with potential to reduce accidents, or even unpleasant and stressful drivingexperience. A higher degree of automated planning also makes it possible to have a betterenergy management strategy with improved performance through analysis of surroundingenvironment of autonomous vehicles and taking action in a timely manner. This thesis deals with planning of autonomous vehicles in different urban scenarios, road,and vehicle conditions. The main concerns in designing the planning algorithms, are realtime capability, safety and comfort. The planning algorithms developed in this thesis aretested in simulation traffic situations with multiple moving vehicles as obstacles. The re-search conducted in this thesis falls mainly into two parts, the first part investigates decou-pled trajectory planning algorithms with a focus on speed planning, and the second sectionexplores different coupled planning algorithms in spatiotemporal environments where pathand speed are calculated simultaneously. Additionally, a behavioral analysis is carried outto evaluate different tactical maneuvers the autonomous vehicle can have considering theinitial states of the ego and surrounding vehicles. Particularly relevant for heavy duty vehicles, the issues addressed in designing a safe speedplanner in the first part are road conditions such as banking, friction, road curvature andvehicle characteristics. The vehicle constraints on acceleration, jerk, steering, steer ratelimitations and other safety limitations such as rollover are further considerations in speedplanning algorithms. For real time purposes, a minimum working roll model is identified us-ing roll angle and lateral acceleration data collected in a heavy duty truck. In the decoupledplanners, collision avoiding is treated using a search and optimization based planner. In an autonomous vehicle, the structure of the road network is known to the vehicle throughmapping applications. Therefore, this key property can be used in planning algorithms toincrease efficiency. The second part of the thesis, is focused on handling moving obstaclesin a spatiotemporal environment and collision-free planning in complex urban structures.Spatiotemporal planning holds the benefits of exhaustive search and has advantages com-pared to decoupled planning, but the search space in spatiotemporal planning is complex.Support vector machine is used to simplify the search problem to make it more efficient.A SVM classifies the surrounding obstacles into two categories and efficiently calculate anobstacle free region for the ego vehicle. The formulation achieved by solving SVM, con-tains information about the initial point, destination, stationary and moving obstacles.These features, combined with smoothness property of the Gaussian kernel used in SVMformulation is proven to be able to solve complex planning missions in a safe way. Here, three algorithms are developed by taking advantages of SVM formulation, a greedysearch algorithm, an A* lattice based planner and a geometrical based planner. One general property used in all three algorithms is reduced search space through using SVM. In A*lattice based planner, significant improvement in calculation time, is achieved by using theinformation from SVM formulation to calculate a heuristic for planning. Using this heuristic,the planning algorithm treats a simple driving scenario and a complex urban structureequal, as the structure of the road network is included in SVM solution. Inspired byobserving significant improvements in calculation time using SVM heuristic and combiningthe collision information from SVM surfaces and smoothness property, a geometrical planneris proposed that leads to further improvements in calculation time. Realistic driving scenarios such as roundabouts, intersections and takeover maneuvers areused, to test the performance of the proposed algorithms in simulation. Different roadconditions with large banking, low friction and high curvature, and vehicles prone to safetyissues, specially rollover, are evaluated to calculate the speed profile limits. The trajectoriesachieved by the proposed algorithms are compared to profiles calculated by optimal controlsolutions.

Download or read book Planning Universal On Road Driving Strategies for Automated Vehicles written by Steffen Heinrich and published by Springer. This book was released on 2018-04-19 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Steffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account. About the Author Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.

Download or read book Autonomous Road Vehicle Path Planning and Tracking Control written by Levent Guvenc and published by John Wiley & Sons. This book was released on 2021-12-06 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest research in path planning and robust path tracking control In Autonomous Road Vehicle Path Planning and Tracking Control, a team of distinguished researchers delivers a practical and insightful exploration of how to design robust path tracking control. The authors include easy to understand concepts that are immediately applicable to the work of practicing control engineers and graduate students working in autonomous driving applications. Controller parameters are presented graphically, and regions of guaranteed performance are simple to visualize and understand. The book discusses the limits of performance, as well as hardware-in-the-loop simulation and experimental results that are implementable in real-time. Concepts of collision and avoidance are explained within the same framework and a strong focus on the robustness of the introduced tracking controllers is maintained throughout. In addition to a continuous treatment of complex planning and control in one relevant application, the Autonomous Road Vehicle Path Planning and Tracking Control includes: A thorough introduction to path planning and robust path tracking control for autonomous road vehicles, as well as a literature review with key papers and recent developments in the area Comprehensive explorations of vehicle, path, and path tracking models, model-in-the-loop simulation models, and hardware-in-the-loop models Practical discussions of path generation and path modeling available in current literature In-depth examinations of collision free path planning and collision avoidance Perfect for advanced undergraduate and graduate students with an interest in autonomous vehicles, Autonomous Road Vehicle Path Planning and Tracking Control is also an indispensable reference for practicing engineers working in autonomous driving technologies and the mobility groups and sections of automotive OEMs.

Download or read book Safe Autonomous and Intelligent Vehicles written by Huafeng Yu and published by Springer. This book was released on 2018-11-14 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the start-of-the-art research and development for the emerging area of autonomous and intelligent systems. In particular, the authors emphasize design and validation methodologies to address the grand challenges related to safety. This book offers a holistic view of a broad range of technical aspects (including perception, localization and navigation, motion control, etc.) and application domains (including automobile, aerospace, etc.), presents major challenges and discusses possible solutions.

Download or read book Safe Trajectories and Sequential Bayesian Decision Making Architecture for Reliable Autonomous Vehicle Navigation written by Dimia Iberraken and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in Autonomous Vehicles (AV) driving raised up all the importance to ensure the complete reliability of AV maneuvers even in highly dynamic and uncertain environments/situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architecture with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks (theoretically, lower than any human-like driving behavior), with a systemic way. Consequently, the aim is also to reduce the need for too extensive testing (which could take several months and years for each produced RAMS without at the end having absolute prove). Hence the goal in this Ph.D. thesis is to have a provable methodology for AV RAMS. This dissertation addresses the full pipeline from risk assessment, path planning to decision-making and control of autonomous vehicles. In the first place, an overall Probabilistic Multi-Controller Architecture (P-MCA) is designed for safe autonomous driving under uncertainties. The P-MCA is composed of several interconnected modules that are responsible for: assessing the collision risk with all observed vehicles while considering their trajectories' predictions; planning the different driving maneuvers; making the decision on the most suitable actions to achieve; control the vehicle movement; aborting safely the engaged maneuver if necessary (due for instance to a sudden change in the environment); and as last resort planning evasive actions if there is no other choice. The proposed risk assessment is based on a dual-safety stage strategy. The first stage analyzes the actual driving situation and predicts potential collisions. This is performed while taking into consideration several dynamic constraints and traffic conditions that are known at the time of planning. The second stage is applied in real-time, during the maneuver achievement, where a safety verification mechanism is activated to quantify the risks and the criticality of the driving situation beyond the remaining time to achieve the maneuver. The decision-making strategy is based on a Sequential Decision Networks for Maneuver Selection and Verification (SDN-MSV) and corresponds to an important module of the P-MCA. This module is designed to manage several road maneuvers under uncertainties. It utilizes the defined safety stages assessment to propose discrete actions that allow to: derive appropriate maneuvers in a given traffic situation and provide a safety retrospection that updates in real-time the ego-vehicle movements according to the environment dynamic, in order to face any sudden hazardous and risky situation. In the latter case, it is proposed to compute the corresponding low-level control based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) that allows the ego-vehicle to pursue the advised collision-free evasive trajectory to avert an accident and to guarantee safety at any time.The reliability and the flexibility of the overall proposed P-MCA and its elementary components have been intensively validated, first in simulated traffic conditions, with various driving scenarios, and secondly, in real-time with the autonomous vehicles available at Institut Pascal.

Download or read book Predictive Control Under Uncertainty for Safe Autonomous Driving written by Ashwin Mark Carvalho and published by . This book was released on 2016 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving vehicles have attracted a lot of interest due to their potential to significantly reduce traffic fatalities and transform people's lives. The reducing costs of advanced sensing technologies and the increasing capabilities of embedded computing hardware have enabled the commercialization of highly automated driving features. However, the reliable operation of autonomous vehicles is still a challenge and a major barrier in the large scale acceptance and deployment of the technology. This dissertation focuses on the challenges of designing safe control strategies for self-driving vehicles due to the presence of uncertainty arising from the non-deterministic forecasts of the driving scene. The overall goal is to unify elements from the fields of vehicle dynamics modeling, machine learning, real-time optimization and control design under uncertainty to enable the safe operation of self-driving vehicles. We propose a systematic framework based on Model Predictive Control (MPC) for the controller design, the effectiveness of which is demonstrated via applications such as lateral stability control, autonomous cruise control and autonomous overtaking on highways. Data collected from our experimental vehicles is used to build predictive models of the vehicle and the environment, and characterize the uncertainty therein. Several approaches for the control design are presented based on a worst-case or probabilistic view of the uncertain forecasts, depending on the application. The proposed control methodologies are validated by experiments performed on prototype passenger vehicles and are executed in real-time on embedded hardware with limited computational power. The experiments show the ability of the proposed framework to handle a variety of driving scenarios including aggressive maneuvers on low-friction surfaces such as snow and navigation in the presence of multiple vehicles.

Download or read book Learning to Drive written by David Michael Stavens and published by Stanford University. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.

Download or read book Decision making Strategies for Automated Driving in Urban Environments written by Antonio Artuñedo and published by Springer Nature. This book was released on 2020-04-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.

Download or read book Path Planning for Autonomous Vehicle written by Umar Zakir Abdul Hamid and published by BoD – Books on Demand. This book was released on 2019-10-02 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Path Planning (PP) is one of the prerequisites in ensuring safe navigation and manoeuvrability control for driverless vehicles. Due to the dynamic nature of the real world, PP needs to address changing environments and how autonomous vehicles respond to them. This book explores PP in the context of road vehicles, robots, off-road scenarios, multi-robot motion, and unmanned aerial vehicles (UAVs ).

Download or read book Semi cooperative Planning in Mixed Human autonomous Environments written by Noam M. Buckman and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles have made immense progress towards deployment on public roads, yet navigating safely on roads with both human drivers and autonomous vehicles presents a challenge for even the most advanced systems. Algorithms and systems are needed for developing and evaluating socially-compliant planning algorithms for autonomous vehicles. In this thesis, we propose a semi-cooperative autonomy framework that considers the underlying social utility of human agents within the vehicle's trajectory planning and motion control. In addition, we present a new robotic platform for deploying and evaluating semi-cooperative autonomy in a safe, laboratory setting. In this thesis, we combine concepts from social psychology with game-theoretic planning algorithms to develop semi-cooperative autonomous planners. Beginning with a single autonomous vehicle, we present Iterative Best Response with Imagined Shared Control, an algorithm that considers the Social Value Orientation of each human driver while achieving desirable game-theoretic equilibria. The semi-cooperative framework is applied to larger scale systems, a socially-compliant intersection manager for mixed human-autonomy traffic and understanding SVO impact on vehicle traffic flow. In addition, we present a visibility-aware trajectory optimization algorithm for proactive motion planning around blind spots, which incorporates a model of human driver uncertainty into a semi-cooperative trajectory planner. We demonstrate the efficacy of these algorithms in simulations of human and autonomous vehicles and study the effect of human personality on algorithm performance. Second, we introduce the MiniCity, a 1/10th scale city environment consisting of realistic urban scenery, intersections, and multiple fully autonomous 1/10th scale vehicles with state-of-the-art sensors and algorithms. We describe how the MiniCity robotic platform is used in the development of semi-cooperative autonomy, from evaluating algorithm performance to developing new intelligent traffic systems. First, we use the MiniCity to evaluate vehicle autonomy, measuring both the impact of upstream perception on downstream vehicle performance and measuring efficiency of semi-cooperative intersection managers. Second, we use the MiniCity's human-in-the-loop driver interface to collect user preferences for co-designing a shared controller for driving through intersections. Finally, we present a novel end-to-end infrastructure-based failure detection algorithm, FailureNet, which is trained and deployed on autonomous vehicles in the MiniCity. In all these, the MiniCity provides a safe and scalable environment for developing interactive algorithms, bringing us closer to fully deploying socially-compliant autonomy on mixed human-autonomous roads.