Vehicle emergency warning and control system

An automatic vehicle warning and control program is provided for determining if safety enhancing actions are appropriate. The on-line determination of an action that results in a preferred outcome (e.g., aircraft ejection) is made using a neural network controller. The neural network controller is trained off-line using appropriate preferred outcome data obtained via computer simulation or experimentation. Appropriate actions are established for all conceivable sets of vehicle conditions. On-line, the neural network controller uses actual sensed vehicle conditions to determine the appropriate action. Various actions can be performed based on the preferred outcome determination. Appropriate actions can include commanding audible and visual warnings, guidance cues, automatic vehicle control, and aircraft automatic ejection.

FIELD OF THE INVENTION 
This invention relates generally to vehicle warning systems and, more 
particularly, to methods and systems for determining when vehicle 
emergency action based on current conditions should take place, and 
performing the appropriate action, for example warning enunciation, 
automatic control of the vehicle, or emergency egress of the vehicle 
occupants. 
BACKGROUND OF THE INVENTION 
Ejection seats for aircraft are known in the art. Minimum altitudes are 
established for controlled aircraft ejections (e.g., 2,000 feet). 
Technology has dramatically improved aircraft ejection seat performance. 
However, human factors continue to adversely affect aircraft ejection 
safety. Several human factors affect safe ejection from an aircraft, for 
example, reaction times, distractions, task management, and altitude 
assessment. Thus, a method for minimizing the effect of human factors on 
aircraft ejection is desirable. The present invention is directed to 
achieving this result. More specifically, the present invention is 
directed to providing a method and system for determining if ejection of 
an occupant from a vehicle is appropriate, and if so, performing automatic 
ejection and/or providing a warning that ejection is appropriate, all 
without operator intervention. 
SUMMARY OF THE INVENTION 
The present invention is directed to a method and system for determining 
whether occupant emergency egress based on current conditions of a vehicle 
is acceptable and warranted. A neural network is created off-line. The 
neural network is implemented in a controller that is used on-line to make 
a vehicle status determination. 
In accordance with other aspects of this invention, an action is performed 
based on the vehicle status determination, which is either that: the 
aircraft can be saved; ejection is acceptable; or ejection is not 
acceptable. If ejection is acceptable, the action performed can be the 
automatic ejection of the occupant from the aircraft. Alternatively, the 
action can be a warning. Warnings or instructions can also serve as the 
function performed when the aircraft can be saved or when ejection is not 
acceptable. 
In accordance with further aspects of the present invention, the off-line 
process of creating a neural network includes: obtaining a set of 
conditions; performing an ejection seat simulation for the set of 
conditions; making a preferred outcome determination; and storing the 
preferred outcome determination. This process is repeated for all 
conceivable sets of conditions.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
While the present invention is ideally suited for an aircraft, and is 
described in the context of an aircraft, it will be appreciated that the 
invention can also be implemented in other types of vehicles, such as 
automobiles and space launch vehicles. 
FIG. 1 illustrates minimum conditions under which the ejection seat(s) of 
an aircraft can be operated to safely eject the occupants of an aircraft 
18. The input parameters, are the aircraft conditions 20 which include 
factors such as: altitude, pitch, roll, flight path angle, angular rates, 
and velocity of the aircraft. Based on these conditions, a determination 
is made as to whether ejection from the aircraft is safe. 
FIG. 2 illustrates in block form the components of a neurocomputing control 
system formed in accordance with the present invention for an aircraft 40. 
The aircraft 40 includes an ejection seat system 42, crew displays 44, 
flight systems 46, sensors and controls 48 for sensing ejection seat 
system and aircraft operation and receiving control commands, and a 
control computer 50 for determining desired vehicle system actions and 
generating appropriate control signals. The control computer 50 includes a 
neural network controller 56 and memory 60. Prior to implementation of the 
neural network controller 56, the neural network controller 56 is trained 
off-line, using a simulation of the aircraft 40 and its ejection seat 
system 42 to acquire knowledge for actual controller operation. The type 
of neural network and training process used is described in more detail 
below with respect to FIGS. 3-4. 
FIG. 3 illustrates the overall logic of the neural network controller 56 
creation process. First, at block 70, an output file is opened. Next, at 
block 72, a new set of aircraft conditions is loaded. An ejection seat 
simulation is then performed at block 74. The ejection seat simulation is 
illustrated in detail in FIG. 4, and described later. Escape decision 
logic is then used to determine if an ejection results in the preferred 
outcome for the set of aircraft conditions loaded. See block 76. Next, at 
block 78, the preferred outcome determined in block 76 is written to the 
output file. As noted, the preferred outcome data is used to determine 
whether or not for the given set of aircraft conditions, an ejection is 
appropriate. The process of loading a new set of aircraft conditions 72, 
performing an ejection seat simulation 74, using the preferred outcome 
decision logic 76 to determine if ejection is appropriate, and writing the 
data to the output file 78 is repeated for sets of aircraft conditions 
that span its entire operating envelope. After sufficient sets of aircraft 
conditions have been considered, the output file is closed in block 79. 
Still referring to FIG. 3, at block 80, the neural network structure is 
established. The neural network structure includes inputs for all sensor 
and control signals (48 of FIG. 2), and outputs for all the corresponding 
command signals that are supplied to the ejection seat, crew displays , or 
flight systems (42, 44, and 46 of FIG. 2, respectively). As shown in FIGS. 
5B and 5C and described more fully below, the neural network structure 
used in the present invention is either a unified or decoupled multi-layer 
perceptron artificial neural network. Next at block 82, the neural network 
structure teaching algorithm generates nodal weight values for the 
established neural network structure based on preferred outcome data 
determined in block 76. Back-propagation algorithms, node decoupled 
extended Kalman filter methods, or other neural network training 
techniques can be used to perform this nodal weight value generation. 
Proper setting of weight values increases the chance that all on-line 
generated data will be accurate. Once all the weight values have been 
generated for the neural network structure, the neural network is 
implemented as a controller in the control computer 50 of the aircraft 40, 
as shown in FIG. 2. See block 84. Neural network operation in a vehicle is 
shown in FIG. 7, and described later. The neural network can be 
implemented as an analog electric circuit, a computer program, an 
application specific integrated circuit or any other structure that allows 
the neural network to function properly. The control computer on which the 
neural network is implemented can be located at various areas within the 
aircraft, for example, in an avionics bay, in an ejection seat controller, 
in a vehicle management system, etc. 
FIG. 4 is an illustrative example of an emergency egress system simulation 
(block 74 of FIG. 3) consisting of an ejection seat. It will be 
appreciated that any high fidelity ejection seat simulation can be used. 
The logic of FIG. 4 moves from a start block to block 90 where an 
initiation signal is detected. Next, in block 92, sensed positions and 
motions are calculated. The types of positions and motions sensed are 
based on the nature of the aircraft (40 in FIG. 2). Examples of positions 
are seat location and roll, pitch, and yaw angles. Examples of motions 
include linear and angular velocity, and acceleration. Maneuver commands 
are then determined in block 94. Next, in block 96, control signals are 
calculated from compensation and control laws. The predefined compensation 
and control laws are determined by the nature of the ejection seat and the 
type of sensors employed. The logic then moves to block 98 where 
propulsion system and control effector forces and moments are calculated. 
Next, in block 100, external environment forces and moments are 
calculated. Then, in block 102, recovery system forces and moments are 
calculated. Next, in block 104, accelerations using mass properties are 
calculated. The logic then moves to block 106 where position and velocity 
are calculated. The logic of FIG. 4 of simulating an ejection seat is 
recursive and ends when the ejection is completely determined. Thus, the 
logic of blocks 90-106 is repeated until ejection is completely 
determined, at which point processing returns to FIG. 3. It will be 
appreciated that the ordering of the functions illustrated in FIG. 4 can 
be rearranged. For example, several of the functions, such as calculating 
external environment forces and moments 100, calculating recovery system 
forces and moments 102 can be performed at the same time as calculating 
propulsion system and control effector forces and moments 98. 
Referring to FIGS. 5A, 5B, and 5C, the neural network includes an input 
layer, one or more hidden layers, and an output layer. The elements that 
make up each layer of a neural network are referred to as neurons or nodes 
120. Inputs are fed forward from the input layer to the hidden layers and 
then to the output layer. Recursive neural network structures may also 
have outputs from the layers fed back to preceding layers. The number of 
neurons 120 in each layer is determined before the network is finally 
trained. Typically, there is one input node for each input variable and 
one output node for each output. The hidden and output layers need not be 
completely connected (FIG. 5C). In a unified neural network (FIG. 5B), 
every input node is connected to every node in the following hidden layer 
and every node in a hidden layer is connected to every node in the 
following adjacent hidden layer or output layer, depending on the number 
of hidden layers. Each connection to a particular node is weighted. 
The number of nodes in the input and output layers is determined before the 
network is trained and corresponds to the number of sensor inputs (48 in 
FIG. 2) and output signals to the ejection seat, displays, or flight 
systems (42, 44, and 46 in FIG. 2 respectively). The number of neurons in 
the one or more hidden layers is selected based on accuracy achievable 
during training. FIG. 6 illustrates a single neuron model of a multi-layer 
perceptron neural network. Equation 1 describes the product of the 
summation 108 performed within the single neuron model 110 shown in FIG. 
6: 
##EQU1## 
where: j=node number 
i=neural network layer number (0 to L) 
k=weight value number 
t=index to an input pattern 
N.sub.i =the number of neurons in the i-th layer of the network 
L=the number of layers in the network 
.omega..sup.i.sub.j,k =k-th weight of the j-th neuron in the i-th layer. 
When k=0, weight value is the bias weight. y.sup.i.sub.j (t)=f(y.sub.-- 
in.sup.i.sub.j (t)) is the product of neuron 110 or nodal activation 
function 112. The nodal activation function 112 is a sigmoidal, radial 
basis, linear or other function. The nodal activation function sets a 
value produced by the neuron or activates or deactivates the neuron 110. 
Derivatives used in the training process of the invention are based on 
expressions for dynamic derivatives of recurrent multi-layer perceptrons. 
Equation 2 which is used in the training process is written as: 
##EQU2## 
where: .delta..sub.i,j =the Kronecker delta. 
If i=j, .delta..sub.i,j =1 and otherwise, .delta..sub.i,j =0. f'(y.sub.-- 
in.sup.i.sub.j) is the derivative of the neuron's activation function 112 
with respect to y.sub.-- in.sup.i.sub.j. 
Equation 3 describes how the weight values are iteratively adjusted using 
derivatives, a learning rate parameter, and error values until the neural 
network outputs closely match the output values contained in the training 
data. 
##EQU3## 
where: 
##EQU4## 
n=learning rate parameter. 
At the beginning of neural network training, the connection weights in the 
network are set to initial values that may be randomized. The training 
data is then presented to the network one set at a time. In accordance 
with the present invention, the training data includes multiple sets of 
aircraft conditions (20 in FIG. 1) and the corresponding values of the 
command signals sent to the ejection seat, crew displays, and flight 
systems for a wide range of vehicle operations. The training process 
continues through all sets of the training data adjusting the weight 
values as necessary. Each set provides its own distinct neural network 
input and output. Accuracy between the values of training data is enhanced 
by applying random components to the training data as is appreciated by 
those of ordinary skill in the art of neural network design. 
Once the neural network structure has been trained, i.e. all the weight 
values have been generated for the neural network structure, the neural 
network structure is implemented as a controller 56 in the control 
computer 50 of the aircraft 40, as shown in FIG. 2. The neural network 
structure can be implemented as an analog electric circuit, a computer 
program, an application specific integrated circuit or any other structure 
that allows the neural network structure to function properly. FIG. 7 
illustrates the on-line process of determining whether an automatic 
aircraft ejection should be performed, using a fully trained neural 
network structure implemented as the controller 56. First, at block 130, 
current conditions of the aircraft are obtained. Next, at block 132, a 
test is made to determine if the aircraft is approaching the ejection 
envelope i.e., aircraft conditions envelope where an ejection is no longer 
permissible. See FIG. 1 for a pictorial representation of a safe ejection 
scenario. The determination is made based on the current and/or predicted 
conditions of the aircraft. The preferred outcome (i.e. escape)decision 
determined in FIG. 3 at block 76 that corresponds to the current 
conditions is produced. Further processing, is based on the retrieved 
decision. If the aircraft is approaching an acceptable ejection envelope, 
a test is made in decision block 136 to determine if the aircraft can be 
saved. If so, an ejection avoidance function is performed. See block 138. 
An example of an ejection avoidance function would be to provide a warning 
(either audio and/or visual) indicating that the state of the aircraft is 
such that the aircraft is about to enter or has entered into a state where 
ejection is unacceptable. Alternatively, instructions for eliminating or 
minimizing the unacceptable ejection state of the aircraft can be provided 
to the operator, or automatically to the flight systems. 
If the aircraft cannot be saved, a test is made in decision block 140 to 
determine if ejection is acceptable. If so, an emergency egress function 
is performed, in block 142. The emergency egress function can be automatic 
ejection. Alternatively, the emergency egress function can be the 
generation of an audio and/or visual message advising ejection. Or, a 
combination of the above can be used, whereby an advisory message will be 
used if the operator is responsive, and automatic ejection will be 
performed if the operator is non-responsive. 
If ejection is not acceptable, an emergency egress not safe function is 
performed, in block 144. For example, a message may be provided indicating 
steps for altering current aircraft state so that an acceptable ejection 
may be possible. If the aircraft is not approaching the ejection envelope, 
or the appropriate function has been performed based on whether the 
aircraft can be saved or whether ejection is acceptable, the logic moves 
to decision block 146 where a test is made to determine if an exit 
condition has been received. If so, processing ends. If not, the logic of 
blocks 130-144 is repeated until an exit signal is received. 
While the preferred embodiment of the invention has been illustrated and 
described, it will be appreciated that various changes can be made therein 
without departing from the spirit and scope of the invention.