In this paper, we present an algorithm for generating complex dynamically feasible maneuvers for autonomous vehicles traveling at high speeds over large distances. Our approach is based on performing anytime incremental search on a multi-resolution, dynamically feasible lattice state space. The resulting planner provides real-time performance and guarantees on and control of the suboptimality of its solution. We provide theoretical properties and experimental results from an implementation on an autonomous passenger vehicle that competed in, and won, the Urban Challenge competition.

/pdf/planning-long-dynamically-feasible-maneuvers-for-autonomous-e6j4s1mfqb.pdf

Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles

A new framework which adopts a rapidly-exploring random tree (RRT) path planner with a Gaussian process (GP) occupancy map is developed for the navigation and exploration of an unknown but cluttere...

A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV

Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AI-based AV systems.

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Sketch of the Analytical Engine invented by Charles Babbage, Esq.

People who design, use, and are affected by autonomous artificially intelligent agents want to be able to trust such agents—that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately. Many techniques have been devised to assess and influence human trust in artificially intelligent agents. However, these approaches are typically ad hoc and have not been formally related to each other or to formal trust models. This article presents a survey of algorithmic assurances, i.e., programmed components of agent operation that are expressly designed to calibrate user trust in artificially intelligent agents. Algorithmic assurances are first formally defined and classified from the perspective of formally modeled human-artificially intelligent agent trust relationships. Building on these definitions, a synthesis of research across communities such as machine learning, human-computer interaction, robotics, e-commerce, and others reveals that assurance algorithms naturally fall along a spectrum in terms of their impact on an agent’s core functionality, with seven notable classes ranging from integral assurances (which impact an agent’s core functionality) to supplemental assurances (which have no direct effect on agent performance). Common approaches within each of these classes are identified and discussed; benefits and drawbacks of different approaches are also investigated.

https://dl.acm.org/doi/pdf/10.1145/3267338

“Dave...I can assure you ...that it’s going to be all right ...” A Definition, Case for, and Survey of Algorithmic Assurances in Human-Autonomy Trust Relationships

Automation in transportation (rail, air, road, etc.) is becoming increasingly complex and interconnected. Ensuring that these sophisticated non-deterministic software systems can be trusted and remain resilient is a community concern. As technology evolves, systems are moving increasingly towards autonomy where the “machine” is intelligent: perceiving, deciding, learning, etc. often without human engagement. Our current mechanisms and policies for oversight and certification of these systems to ensure they operate robustly in safety-critical situations are not keeping pace with technology advancements. How is an autonomous system different than an advanced automatic system? How is trust different for an autonomous system? What are different perspectives on trust? Is it appropriate to apply the techniques used to establish trust in a today’s transportation systems to establishing trust in an autonomous system? This paper will examine these questions and propose a framework for discussing autonomy assurance and trust in transportation applications. We will explore further with two examples: 1) the notion of a self-driving taxi-cab; and 2) the evolution of a two-pilot flight deck, to single-pilot operations.

Autonomy, Trust, and Transportation

In this paper, we describe the design and development of a network of large unmanned ground vehicles (LUGVs) using low-cost, off-the-shelf technology and building upon experience gained at the 2005 Defense Advanced Research Projects Agency (DARPA) Grand Challenge. Our unique distributed architecture allows robots with different capabilities to interact, facilitates the use of multiple programming languages, and supports location and employment transparency of the interacting software agents. We wrote safety-critical and real-time software in Ada to benefit from the language's precise semantics and timing control, while writing navigation, maneuvering, user interaction, and visualization software in Java for rapid, flexible development.

A distributed, multi-language architecture for large unmanned ground vehicles

The MITRE Corporation's (McLean, VA) Meteor team fielded an autonomous vehicle that competed in DARPA's 2005 Grand Challenge race. This paper describes the team's approach to building its robotic vehicle, the vehicle and components that let the vehicle see and act, and the computer software that made the vehicle autonomous. Also presented are details of how the team prepared for the race and how their vehicle performed. © 2006 Wiley Periodicals, Inc.

MITRE Meteor: An off-road autonomous vehicle for DARPA's Grand Challenge: Field Reports

The MITRE Meteor team fielded an autonomous vehicle that competed in DARPA’s 2005 Grand Challenge race. This paper describes the team’s approach to building its robotic vehicle, the vehicle and components that let the vehicle see and act, and the computer software that made the vehicle autonomous. It presents how the team prepared for the race and how their vehicle performed.

/pdf/mitre-meteor-an-off-road-autonomous-vehicle-for-darpa-s-4p7u4dpqj8.pdf

MITRE Meteor: An Off-Road Autonomous Vehicle for DARPA’s Grand Challenge

The Defense Advanced Research Projects Agency (DARPA) challenged autonomous ground vehicle developers in the "2005 DARPA Grand Challenge" to build a vehicle that could complete a 132 mile course through the American desert southwest. MITRE, a not-for-profit systems engineering company, responded to this challenge by creating the MITRE Meteor in just 11 months. This rapid development relied on software employment transparency to get the maximum utility out of each line of code. Judicious design of the software framework allowed the same body of code to animate the robot in the field, support laboratory experimentation, and analyze recorded field testing data. This paper describes how software employment transparency was achieved and how it increased development efficiency.

/pdf/the-mitre-meteor-robot-control-software-simulate-as-you-3nzjln9d7m.pdf

The mitre meteor robot control software: simulate as you operate

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