Adaptive Airborne Separation to Enable UAM Autonomy in Mixed Airspace

The excitement and promise generated by Urban Air Mobility (UAM) concepts have inspired both new entrants and large aerospace companies throughout the world to invest hundreds of millions in research and development of air vehicles, both piloted and unpiloted, to fulfill these dreams. The management and separation of all these new aircraft have received much less attention, however, and even though NASAs lead is advancing some promising concepts for Unmanned Aircraft Systems (UAS) Traffic Management (UTM), most operations today are limited to line of sight with the vehicle, airspace reservation and geofencing of individual flights. Various schemes have been proposed to control this new traffic, some modeled after conventional air traffic control and some proposing fully automatic management, either from a ground-based entity or carried out on board among the vehicles themselves. Previous work has examined vehicle-based traffic management in the very low altitude airspace within a metroplex called UTM airspace in which piloted traffic is rare. A management scheme was proposed in that work that takes advantage of the homogeneous nature of the traffic operating in UTM airspace. This paper expands that concept to include a traffic management plan usable at all altitudes desired for electric Vertical Takeoff and Landing urban and short-distance, inter-city transportation. The interactions with piloted aircraft operating under both visual and instrument flight rules are analyzed, and the role of Air Traffic Control services in the postulated mixed traffic environment is covered. Separation values that adapt to each type of traffic encounter are proposed, and the relationship between required airborne surveillance range and closure speed is given. Finally, realistic scenarios are presented illustrating how this concept can reliably handle the density and traffic mix that fully implemented and successful UAM operations would entail

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018. Video summary: http://youtu.be/ebIrBn_nc-

A new strategy of detecting traffic information based on traffic camera : modified inverse perspective mapping

The development of Intelligent Transportation Systems (ITS) needs high quality traffic information such as intersections, but conventional image-based traffic detection methods have difficulties with perspective and background noise, shadows and lighting transitions. In this paper, we propose a new traffic information detection method based on Modified Inverse Perspective Mapping (MIPM) to perform under these challenging conditions. In our proposed method, first the perspective is removed from the images using the Modified Inverse Perspective Mapping (MIPM); afterward, Hough transform is applied to extract structural information like road lines and lanes; then, Gaussian Mixture Models are used to generate the binary image. Meanwhile, to tackle shadow effect in car areas, we have applied a chromacity-base strategy. To evaluate the performance of the proposed method, we used several video sequences as benchmarks. These videos are captured in normal weather from a high way, and contain different types of locations and occlusions between cars. Our simulation results indicate that the proposed algorithms and frameworks are effective, robust and more accurate compared to other frameworks, especially in facing different kinds of occlusions

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed