NASA Automated Rendezvous and Capture Review. Executive summary

In support of the Cargo Transfer Vehicle (CTV) Definition Studies in FY-92, the Advanced Program Development division of the Office of Space Flight at NASA Headquarters conducted an evaluation and review of the United States capabilities and state-of-the-art in Automated Rendezvous and Capture (AR&C). This review was held in Williamsburg, Virginia on 19-21 Nov. 1991 and included over 120 attendees from U.S. government organizations, industries, and universities. One hundred abstracts were submitted to the organizing committee for consideration. Forty-two were selected for presentation. The review was structured to include five technical sessions. Forty-two papers addressed topics in the five categories below: (1) hardware systems and components; (2) software systems; (3) integrated systems; (4) operations; and (5) supporting infrastructure

Towards a Smart World: Hazard Levels for Monitoring of Autonomous Vehicles’ Swarms

This work explores the creation of quantifiable indices to monitor the safe operations and movement of families of autonomous vehicles (AV) in restricted highway-like environments. Specifically, this work will explore the creation of ad-hoc rules for monitoring lateral and longitudinal movement of multiple AVs based on behavior that mimics swarm and flock movement (or particle swarm motion). This exploratory work is sponsored by the Emerging Leader Seed grant program of the Mineta Transportation Institute and aims at investigating feasibility of adaptation of particle swarm motion to control families of autonomous vehicles. Specifically, it explores how particle swarm approaches can be augmented by setting safety thresholds and fail-safe mechanisms to avoid collisions in off-nominal situations. This concept leverages the integration of the notion of hazard and danger levels (i.e., measures of the “closeness” to a given accident scenario, typically used in robotics) with the concept of safety distance and separation/collision avoidance for ground vehicles. A draft of implementation of four hazard level functions indicates that safety thresholds can be set up to autonomously trigger lateral and longitudinal motion control based on three main rules respectively based on speed, heading, and braking distance to steer the vehicle and maintain separation/avoid collisions in families of autonomous vehicles. The concepts here presented can be used to set up a high-level framework for developing artificial intelligence algorithms that can serve as back-up to standard machine learning approaches for control and steering of autonomous vehicles. Although there are no constraints on the concept’s implementation, it is expected that this work would be most relevant for highly-automated Level 4 and Level 5 vehicles, capable of communicating with each other and in the presence of a monitoring ground control center for the operations of the swarm

Traffic-Aware Ecological Cruising Control for Connected Electric Vehicle

The advent of intelligent connected technology has greatly enriched the capabilities of vehicles in acquiring information. The integration of short-term information from limited sensing range and long-term information from cloud-based systems in vehicle motion planning and control has become a vital means to deeply explore the energy-saving potential of vehicles. In this study, a traffic-aware ecological cruising control (T-ECC) strategy based on a hierarchical framework for connected electric vehicles in uncertain traffic environments is proposed, leveraging the two distinct temporal-dimension information. In the upper layer that is dedicated for speed planning, a sustainable energy consumption strategy (SECS) is introduced for the first time. It finds the optimal economic speed by converting variations in kinetic energy into equivalent battery energy consumption based on long-term road information. In the lower layer, a synthetic rolling-horizon optimization control (SROC) is developed to handle real-time traffic uncertainties. This control approach jointly optimizes energy efficiency, battery life, driving safety, and comfort for vehicles under dynamically changing traffic conditions. Notably, a stochastic preceding vehicle model is presented to effectively capture the uncertainties in traffic during the driving process. Finally, the proposed T-ECC is validated through simulations in both virtual and real-world driving conditions. Results demonstrate that the proposed strategy significantly improves the energy efficiency of the vehicle

Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption