Browse Topic: Aircraft collision avoidance systems
This document presents criteria for flight deck controls and displays for Airborne Collision Avoidance Systems.
The proliferation of Autonomous Aerial Vehicles (AAVs) necessitates robust solutions for dynamic obstacle avoidance, particularly against non-cooperative intruders whose trajectories are unpredictable. While traditional path-planning algorithms excel in static environments, they struggle with dynamic obstacles due to the inherent difficulty in accurately estimating and registering their real-time depth and velocity into a world model. This paper presents a novel two-stage vision-based framework that leverages deep learning for reactive avoidance of non-cooperative dynamic intruders. Our approach decouples the perception and decision-making processes: an object detection deep neural network first processes monocular camera images to detect and track the 2D pixel coordinates of intruders. This perceptual output is then fed into a deep reinforcement learning agent, which learns a mapping from the intruder's image-space location to a high-level avoidance maneuver. This leads to more efficient learning, as the RL agent focuses solely on the policy without the burden of learning visual features. The advantage of using RL lies in its ability to handle partially observable situations—because reliable depth or full 3-D position information is not always readily available from monocular imagery, the RL agent learns to act based on the observable visual cues. Simulation results confirm that our proposed framework provides an effective solution for vision-based, non-cooperative intruder avoidance.
Advanced Air Mobility (AAM) is an innovative concept that aims to revolutionize air transportation through electric and unmanned aircraft, enabling applications such as urban air taxis and medical transport. However, one of the key challenges to its widespread adoption is ensuring safety, particularly in collision avoidance. This study focuses on the development of a perception and guidance system for avoiding collisions with non-cooperative targets, which do not share their position or trajectory. To achieve this, a Frequency-Modulated Continuous Wave (FMCW) radar and an InfraRed(IR) camera are used. Compared to traditional pulsed or panel radars, FMCW radars offer higher resolution, better detection of small and slow-moving objects, and improved performance in cluttered environments. The IR camera enhances situational awareness by providing visual confirmation and additional tracking capability, making this sensor fusion approach particularly suitable for AAM applications. Our collision avoidance system follows ACAS Xu standards, which provide autonomous conflict detection and resolution for unmanned aerial vehicles. The maneuver selection process is based on precomputed lookup tables generated through a Markov Decision Process (MDP), optimizing responses based on risk and energy consumption. The entire system is tested in a simulation environment using Ansys AVxcelerate, a physics-based simulator capable of generating realistic sensor data. This approach allows for comprehensive testing of detection, tracking, and maneuver execution in a highly realistic scenario, ensuring the effectiveness of the proposed solution before real-world deployment.
Flight Testing Detect and Avoid (DAA) Systems
Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL technology could offer timely, on-demand, and potentially cost-effective aerial mobility capabilities to counter these challenges. Early detection and suppression of wildfires could prevent many fires from becoming large-scale disasters. eVTOL aircraft may not have the capacity of larger aerial assets for firefighting, but targeted suppression, potentially in swarm operations, could be valuable. Most importantly, on-demand aerial extraction of firefighters can be a crucial benefit during wildfire control operations. Aerial firefighter dispatch from local fire stations or vertiports can result in more effective operations, and targeted aerial fire suppression and civilian extraction from high-rise buildings could enhance capabilities significantly. There are some challenges that need to be addressed before the identified capabilities and benefits are realized at scale, including the development of firefighting-specific eVTOL vehicles; sense and avoid capabilities in complex, smoke-inhibited environments; autonomous and remote operating capabilities; charging system compatibility and availability; operator and controller training; dynamic airspace management; and vehicle/fleet logistics and support. Acceptance from both the first-responder community and the general public is also critical for the successful implementation of these new capabilities.
In this article, a formation flying technique designed for a multiple unmanned aerial vehicles (multi-UAV) system to provide low-cost and efficient solution for civilian and military applications is presented. First, a modular leader-follower formation algorithm was developed to accomplish the formation flying with off-the-shelf low-cost components and sensors. Second, a proportional-integral-derivative (PID) controller was utilized for velocity control of the UAVs to maintain the tight formation. Third, a particle swarm optimization-optimized reciprocal velocity obstacles (PSO-RVO) algorithm was utilized for obstacles avoidance and collision avoidance between the UAVs while navigating, with the aid of sonar ranging sensors onboard. The formation flying algorithm developed was tested through both simulation and experiment using two quadcopters with global positioning system (GPS) signals. For the simulation, the algorithm developed was tested on a virtual quadcopter using an open-source software-in-the-loop (SITL) simulator. With the aid of the experimental test, the effectiveness of the proposed formation flying algorithm is evaluated. With a separation distance of 5 m between the UAVs, the proposed system is able to achieve an average separation error of 0.3872 m and percentage of root mean square error (RMSE) of 9.7%. Therefore, it is shown that the proposed formation flying system is very effective.
Aiming at the problem of poor robustness after the combination of lateral kinematics control and lateral dynamics control when an autonomous vehicle decelerates and changes lanes to overtake at a certain distance. This paper proposes a trajectory determination and tracking control method based on a PI-MPC dual algorithm controller. To describe the longitudinal deceleration that satisfies the lateral acceleration limit during a certain distance of lane change, firstly, a fifth-order polynomial and a uniform deceleration motion formula are established to express the lateral and longitudinal displacements, and a model prediction controller (MPC) is used to output the front wheel rotation angle. Through the dynamic formula and the speed proportional-integral (PI) controller to control and adjust the brake pressure. Based on simulation to optimize the best lane change completion time coefficient at different longitudinal lane change speeds, the relationship between the vehicle collision avoidance stable lane change time and the real-time vehicle speed and deceleration is obtained, then it is optimized by neural network algorithm, to avoid the vehicle collision avoidance and deceleration change unstable performance such as rollover occurred during the road. Finally, the simulation verification of the deceleration and lane changing to overtake conditions at a certain initial vehicle speed shows that the maximum lateral acceleration is 3.03m/s2, and the error from the maximum allowable acceleration is 1%. The maximum error of the yaw angle is 0.8°, and the maximum lateral acceleration is 3.22m/s2 and 3.16m/s2 respectively, which does not exceed the allowable acceleration of 4m/s2, which satisfies the lateral stability of the vehicle. Therefore, in the study of trajectory planning and tracking control of autonomous vehicles, the controller can improve the control robustness of decelerating and changing lanes.
Unmanned aerial vehicles (UAVs) are envisioned to operate much closer to each other in low-altitude airspace than in the conventional high-altitude air traffic system and therefore impose challenges not only to the vehicle design but also to the development of a safe yet efficient low-altitude air traffic system. NASA Ames developed an air traffic simulation tool known as Flexible engine for Fast time evaluation of Flight environments (Fe3).
Researchers developed a sensor and software application to detect and avoid energized power lines in the vicinity of unmanned aerial systems (UAS). The goal is to provide drones sufficient time and distance to react, avoid wires, and navigate follow-on maneuvers.
This study provides a simulation-based comparative analysis of the distance and time needed for long combination vehicles (LCVs) - namely, A-doubles with 28-, 33-, and 48-ft trailers - to safely exercise an emergency, evasive steering maneuver such as required for obstacle avoidance. The results are also compared with conventional tractor-semitrailers with a single 53-ft trailer. A multi-body dynamic model for each vehicle combination is developed in TruckSim® with an attempt to assess the last point to steer (LPTS) and evasive time (ET) at various highway speeds under both dry and wet road conditions. The results indicate that the minimum avoidance distance and time required for the 28-ft doubles vary from 206 ft (60 mph) to 312 ft (80 mph) and 2.3 s to 2.6 s, respectively. The required LPTS represents a 6% to 31% increase when compared with 53-ft semitrucks. When driving below 76 mph on a dry road and below 75 mph on a wet road, the 28-ft doubles exhibit LPTS and ET that are larger than 33-ft doubles. In addition, the 33-ft doubles exhibit larger LPTS and ET than 48-ft doubles for the highway speeds considered. This is mainly attributed to the longer trailer wheelbase that causes smaller rear trailer amplifications. At speeds higher than 76 mph on dry roads and 75 mph on wet roads, however, an opposite trend is observed. As the trailer length increases, the distance and time needed to safely avoid an obstacle also increase. A comparison between dry and wet road conditions is also conducted, with the results indicating that more time and distance would be needed for obstacle avoidance on wet roads.
Contemporary air traffic management (ATM) challenges are both (1) acute and (2) growing at rates far outpacing established ways for absorbing technological innovation. Lack of timely response will guarantee failure to meet demands. Immediately that creates a necessity to identify means of coping and judging new technologies based on possible speed of adoption. Paralleling the challenges are developments in capability, both recent and decades old. Some steps (e.g., Global Positioning System (GPS) backup) are well known and, in fact, should have progressed further long ago. Others (e.g., sharing raw measurements instead of position fixes) are equally well known and, if followed by further flight tests initiated (and successful) years ago, would have produced a wealth of in-flight experience by now if development had continued. Other possibilities (e.g., automated pilot override) are much less common and are considered largely experimental. This SAE EDGE™ Research Report is aimed at focusing industry attention on unsettled ATM issues and activities that appear most likely to offer solutions, starting with the near term and continuing on toward increasing versatility and confidence as experience accumulates. In general, the more familiar developments tend to suggest quicker acceptance of test trial initiation, while comparatively unexplored techniques call for a more gradual assimilation. Flexibility for growth is needed in any event, without the pervasive delays that have obstructed progress for so long. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the challenges they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio.
The bus sector is currently lagging behind when it comes to implementing autonomous systems for improved vehicle safety. However, in cities such as London, public transport strategies are changing, with requirements being made for advanced driver-assistance systems (ADAS) on buses. This study discusses the adoption of ADAS systems within the bus sector. A review of the on-road ADAS bus trials shows that passive forward collision warning (FCW) and intelligent speed assistance (ISA) systems have been successful in reducing the number of imminent pedestrian/vehicle collision events and improving speed limit compliance, respectively. Bus accident statistics for Great Britain have shown that pedestrians account for 82% of all fatalities, with three quarters occurring with frontal bus impacts. These statistics suggest that the bus forward collision warning system is a priority for inclusion in future vehicles to enhance the driver’s direct vision, and to increase reaction time for earlier brake application. Almost 80% of bus occupant casualties occurred in non-impact situations, mainly during acceleration/deceleration events. Therefore, care must be taken in implementing autonomous braking in buses, to ensure that it does not cause an increased number of deceleration events beyond the safe stability limits for passengers. Real on-road drive cycle data has shown that while instances of unsafe braking events do not occur regularly, there are instances of braking events that would present a hazard to both seated and standing passengers, therefore systems that would mitigate these issues would have real benefits to both passenger comfort and safety. During tests to simulate the use of the vehicle retarder for an autonomous braking system, deceleration rates largely remained safely within standee and seated passenger stability limits, whereas an emergency stop test showed a peak deceleration 3.5 times the limit of a standee supported by a vertical handrail, and 4 times the limit for a forward/backward facing seated passenger.
This paper intends to present a novel optimal trajectory planning method for obstacle avoidance on highways. Firstly, a mapping from the road Cartesian coordinate system to the road Frenet-based coordinate system is built, and the path lateral offset in the road Frenet-based coordinate system is represented by a function of quintic polynomial respecting the traveled distance along the road centerline. With different terminal conditions regarding its position, heading and curvature of the endpoint, and together with initial conditions of the starting point, the path planner generates a bunch of candidate paths via solving nonlinear equation sets numerically. A path selecting mechanism is further built which considers a normalized weighted sum of the path length, curvature, consistency with the previous path, as well as the road hazard risk. The road hazard is composed of Gaussian-like functions both for the obstacle and road boundaries, which means, if one path is near the obstacle or road boundaries, the driving risk would become large and the path would not be preferred chosen. Then the optimal collision-free path would be transformed back to the road Cartesian coordinate system and used for tracking by the path following module. Moreover, the speed profile along with the optimal path which is also based on polynomials respecting the traveled distance is determined by the multi-object optimization technique, which incorporates the driving comfort and safety simultaneously. Finally, several scenarios for obstacle avoidance on different shapes of the highway are simulated to verify the effectiveness of the proposed framework.
Lane-changing is a typical traffic scene effecting on road traffic with high request for reliability, robustness and driving comfort to improve the road safety and transportation efficiency. The development of connected autonomous vehicles with V2V communication provide more advanced control strategies to research of lane-changing. Meanwhile, four-wheel steering is an effective way to improve flexibility of vehicle. The front and rear wheels rotate in opposite direction to reduce the turning radius to improve the servo agility operation at the low speed while those rotate in same direction to reduce the probability of the slip accident to improve the stability at the high speed. Hence, this paper established Four-Wheel-Steering(4WS) vehicle dynamic model and quasi real lane-changing scenes to analyze the motion constraints of the vehicles. Then, the polynomial function was used for the lane-changing trajectory planning and the extended rectangular vehicle model was established to get vehicle collision avoidance condition. Vehicle comfort requirements and lane-changing efficiency were used as the optimization variables of optimization function and the control of trajectory tracking can be obtained by using model predictive control (MPC) method. A lane-changing model based on steering characteristics and safety distance with the system of V2V communication and collaboration strategy was established. The lane-changing trajectory was simulated by MATLAB and the results showed that the lane-changing trajectory can safely realize the lane-changing behavior of 4WS autonomous vehicles.
This document presents criteria for flight deck controls and displays for Airborne Collision Avoidance Systems.
Most of today’s collision-avoidance, in-flight-entertainment (IFE), air-to-ground-communications, and other avionics systems employ electronics packaging based on the Aeronautics Radio INC (ARINC) 600 standard. Compared to the older ARINC 404 standard dating from the 1970s that defined “black box” enclosures and racks within aircraft, ARINC 600 specified a Modular Concept Unit (MCU) – the basic building block module for avionics. An ARINC 600 metal enclosure can hold up to 12 MCUs, allowing a lot of computing power to be placed in a centralized “box.” By making it possible to run numerous applications over a real-time network, ARINC 600 enabled “next generation” integrated modular avionics (IMA).
To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time. The preparation driving plans are supported by preparation strategy on which the vehicle simulates each hazard before detecting hazards and they are safer than the response driving plans but it is not always efficient. The theory suggests that applicative driving plan of automated vehicle is as follows: 1) the automated vehicle takes the response driving plan when there is a switchable preparation driving plan, 2) the automated vehicle takes the preparation driving plan that is safe but low-efficient compared with the response driving plan, otherwise. The theory also shows that any driving plan need to assume the upper limit of hazard growing speed of traffic environment in order to achieve the practical application of automated vehicles. How to determine the upper limit is a remaining problem, which is a social problem rather than a technical problem.
Spinoff is NASA's annual publication featuring successfully commercialized NASA technology. This commercialization has contributed to the development of products and services in the fields of health and medicine, consumer goods, transportation, public safety, computer technology, and environmental resources.
The effectiveness of ADAS addressing property damage has an increasing impact on car manufacturers, insurers and customers, as accident avoidance or mitigation can lead to loss reduction. In order to obtain benefits, it is essential that ADAS primarily address monetarily relevant accident scenarios. Furthermore, sensor technologies and algorithms have to be configured in a way that relevant accident situations can be sufficiently avoided at reasonable system costs. A new methodology is developed to identify and configure monetarily effective parameters for ADAS during parking and maneuvering. ADAS parameters e.g. relevant accident scenarios, required crash avoidance speeds and different sensor layouts are analyzed and evaluated using a real-world in-depth accident database of insurance claims provided by Allianz Center for Technology and Allianz Automotive Innovation Center. For this purpose, a sensitivity analysis is conducted to identify most monetarily effective accident scenarios. Furthermore, a new cost-benefit approach is pursued, to determine monetarily effective parameter configurations. For each configuration, e.g. high crash avoidance speed vs. low crash avoidance speed, the corresponding monetary effectiveness is evaluated. To avoid over-engineering ADAS functionalities, parameter configurations with the highest incline in monetary effectiveness are determined using derivation. The proposed new method allows an economic development of monetarily effective ADAS and is applied to the example of a luxury class vehicle and an exemplary 360° parking and maneuvering emergency braking system.
Advanced driver assistance systems (ADAS) are improving driver and pedestrian safety, providing vehicle capabilities such as pedestrian detection, lane departure warnings, collision avoidance, and much more. The increasing use of cameras throughout vehicles is enabling many ADAS capabilities. For ADAS applications involving cameras, one critical design challenge is to move image data from the camera to the processing unit and from the processing unit to each display as quickly and efficiently as possible.
Electroimpact, in collaboration with Boeing, has developed an advanced robotic assembly cell, dubbed “The Quadbots.” Using Electroimpact’s patented Accurate Robot technology and multi-function end effector (MFEE), each robot can drill, countersink, inspect hole quality, apply sealant, and insert fasteners into the part. The cell consists of 4 identical machines simultaneously working on a single section of the Boeing 787 fuselage, two on the left, and two on the right. These machines employ “collision avoidance” a new feature in their software to help them work more synchronously. The collision avoidance software uses positional feedback from external safety rated encoders mounted to the motors on the robot. From this feedback, safe spaces, in the form of virtual boundaries can be created. Such that a robot will stop and wait if the adjacent robot is in, or going to move into its programmed work envelope. Another feature of the collision avoidance is to limit robot speeds when they are occupying a zone slightly off the surface of the part. This allows the skin of the part to provide safe guarded space within the fuselage section, allowing a human element to be present. This is critical to the process as technicians run collars on each fastener before the machine moves onto the next hole to ensure adjacent hole clamp up for one up assembly. The production rate for this cell is so demanding, a 5th identical robot is required so that preventative maintenance can be done without interrupting production.
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