https://link.springer.com/chapter/10.1007/978-3-030-62056-1_42

1. Don’t go that way! Risk-aware Decision Making for
Autonomous Vehicles
Kasra Mokhtari1
, Kendra A. Lang2
, and Alan R. Wagner1
1
The Pennsylvania State University, State College, PA 16802, USA
2
Verus Research, Albuquerque, NM, 87109 USA
{kbm5402,alan.r.wagner}@psu.edu
kendra.lang@verusresearch.net
Abstract. Accurately predicting risk or potentially risky situations is critical for
the safe operation of autonomous systems. Risk refers to the expected likelihood
of an undesirable outcome, such as a collision. We draw on an existing conceptu-
alization of the risk to evaluate a robot’s options (e.g. choice of a path to travel).
In this context, risk consists of two components: 1) the probability of an unde-
sirable outcome computed by a Bayesian Network (BN) and 2) an estimate of
the loss associated with the undesirable outcome. We demonstrate that our risk
assessment tool is effective at computing the anticipated risk over a wide variety
of the robot’s options and selecting the option with the lowest risk for two differ-
ent types of autonomous systems: an Autonomous Vehicle (AV) operating near
a college campus and a pair of Unmanned Aerial Vehicles (UAVs) flying from
Washington DC to Baltimore. The method for assessing risk is used to identify
higher risk routes, days to travel, and travel times for an autonomous vehicle and
higher risk routes for a UAV.
Keywords: Risk assessment · Bayesian Network · Autonomous systems.
1 Introduction
An increasing number of robots and robotic applications are being developed that will
allow autonomous systems to come into contact with the public. While many of these
present little or no risk to people, some applications, such as autonomous driving and
aerial vehicles, can pose physical, even fatal, threats to pedestrians. For Autonomous
Vehicles (AVs) in particular, and any large mobile platform navigating in public spaces,
concern for and protection from accidents should be a paramount concern. And al-
though these systems tend to have a wide array of safety features, we believe that risk-
centric prediction and high-risk avoidance are two elements that appear to be under-
studied in the existing literature.
This paper focuses on risk. Specifically, we seek to develop tools that will allow an
autonomous system to predict risk or potentially risky situations and avoid them. Our
work is motivated by some of the simple tasks that AVs still seem incapable or disin-
clined to do. Many college campuses and urban environments in general, for example,
see a surge in pedestrian traffic prior to classes or after classes. These pedestrians may
not obey traffic signals and tend not to pay attention to the vehicles on the road. Rather

2. 2 K. Mokhtari et al.
than attempting to wade through a large number of inattentive pedestrians it would
be advantageous if an AV could identify the increased risk associated with the greater
pedestrian foot traffic and avoid it.
Risk is traditionally described as the expected likelihood of an undesirable out-
come [4]. For robots, because of their embodiment, an undesirable outcome can be a
fatal crash or an accident that involves property damage. Risk is often characterized by
two components: 1) the probability of an undesirable outcome and 2) an estimate of
how undesirable the outcome is (loss). Our approach therefore considers not only the
probability of an accident, but also the cost of that accident. While it may be unpleasant
to discuss, courts and society in general often place specific, usually monetary value
on lives and property. Our approach makes use of these types of legal tools to allow a
vehicle to select the lesser of several evils. This paper contributes a general approach
to risk-aware decision making for autonomous systems. We believe that our approach
is novel in that it allows for the inclusion of a wide variety of risk types for different
vehicles operating in different situations. We feel that the impact of this work has the
potential to stem beyond autonomous driving, allowing other robotic applications to
identify potentially risky behaviors or options and avoid them. This ability could be
vital for social robot construction or military applications.
The remainder of the paper is organized as follows: Section 2 reviews the related
work regarding risk assessment for autonomous systems. Section 3 describes our risk
assessment methodology to evaluate an autonomous system’s options. Section 4 em-
pirically demonstrates how the risk assessment method might inform the autonomous
system’s decision-making process. Section 5 provides the experimental results and dis-
cussion. Finally, Section 6 offers conclusions and directions for future research.
2 Related Work
This section presents prior research related to assessing and using risk by an autonomous
system, divided into risk assessment for Autonomous Vehicles (AVs) and risk assess-
ment for Unmanned Aerial Vehicles (UAVs).
2.1 Risk Assessment for Autonomous Vehicles
A great deal of research related to risk and autonomous vehicles focuses on minimiz-
ing the risk of a collision. Greytak and Hover develop a motion planning controller
that incorporates the risk of a collision in its planning algorithm [11]. Chinea and Par-
ent [5] attempted to assess the risk of a collision by training a Recursive Neural Net-
work (RNN) from simulated driving data. Risk is quantified in terms of the actions
and objects present at road intersections including vehicles, pedestrians, buildings, etc.
Strickland, Fainekos, and Amor train a deep predictive model on simulated intersection
data [15]. The reliance on simulated data, however, brings into question if this approach
will translate to real-world scenarios. Yu, Vasudevan, and Johnson-Roberson use a Par-
tially Observable Markov Decision Process (POMDP) to characterize environments that
include occlusions and evaluate their method in terms of collision rate and ride comfort

3. Don’t go that way! Risk-aware Decision Making for Autonomous Vehicles 3
on simulated and real-world data [18]. David, Lancz, and Hunyady explore the risk as-
sociated with rapid maneuvers and use a similar risk formulation as our own [8]. Their
work also focuses on risk classification.
This aforementioned work, although related, generally attempts to use risk to influ-
ence the immediate vehicle reactions to a pending collision (collision avoidance), rather
than to influence higher-level planning to avoid risky situations. Our approach, on the
other hand, offers a general framework that could incorporate these related strategies as
well as other types of risk using a variety of specialized computational strategies.
2.2 UAV Risk Assessment
Risk has also been used heavily in the assessment of autonomous UAV flight. For in-
stance, risk has been considered as a factor for planning and control [10], the calculation
of casualty rates from accidents and ground impacts [7]. A minimum risk path planning
approach for UAVs in the presence of orthographic obstacles is presented in [9]. In this
paper, the risk factor is simply characterized by the distance of the UAVs to the obsta-
cle. Rudnick-Cohen et al. use risk and flight time to optimize UAV path planning [14].
They also measured the number of possible deaths at a potential crash location as a
determinant of risk in the case of the terrain impact. This location is identified using a
simulation. A less conservative yet more realistic risk management model is discussed
in [17] by leveraging a penetration factor that accounts for smaller UAVs which pose a
lower risk and obstacles that offer cover from falling objects in that area. Risk models
have been developed for a variety of UAV operations. These tools are used as a regu-
latory method [10] and as a tool for ground operations management [16]. These risk
models might also provide useful information to the system we are proposing.
Bayesian networks are commonly used for quantitative risk assessment [12]. A
quantitative risk analysis for UAVs is described in [1] although risk is simply described
as the probability of a crash. The Unmanned aircraft system traffic management Risk
Assessment Framework (URAF) developed in [2] computes risk to third parties based
on the potential impact area and the consequences of the impact. Barr et al. implemented
a probabilistic model-based technique to estimate risk for UAVs based on multi-factor
interdependencies and their failure modes along with other parameters such as environ-
mental factors and aircraft failure types [3].
In this paper, by using a Bayesian Network and taking into consideration causal re-
lationships and conditional probabilities, a general framework is offered which can also
be used to create different types of crash accident models. The above related work tends
to rely on simple instantiations of risk (e.g., the distance of a UAV to an obstacle, the
number of possible deaths resulting from a crash, etc.). In contrast, the work presented
here defines and uses risk by integrating a loss function with the conditional probability
of various undesirable events, and then selects courses of action in order to minimize
that risk. In the context of UAVs, we use a mid-air collision and a terrain impact as the
undesirable events, whose likelihoods are computed from a Bayesian network. The loss
functions are calculated according to the price of the UAV and the estimated price of
the structures over which the UAVs will be flying. With safety as a top priority, this risk
assessment tool can be used for high-level planning for UAVs to avoid risky pathways.

4. 4 K. Mokhtari et al.
3 Using Risk to Evaluate a Robot’s Options
Risk is traditionally described as the expected value of an undesirable outcome. For-
mally, risk is defined as [4]:
R(x) = ΣyL(x, y)p(y) (1)
where L(x, y) is the loss associated with choosing action x when event y occurs, and
p(y) is the probability of event y occurring. For the work presented here, event y cor-
responds to various possible failures of the system. Since this work focuses on the risk
associated with travel along a path, action x corresponds to choosing one path for the
vehicle among a set of potential paths. The path of the vehicle is discretized into N time
steps and we use a Bayesian network to estimate the probability of event y at each time
step given the inputs to the network. Therefore, the risk associated with choosing the
path x at time step i denoted by Ri(x) is calculated as:
Ri(x) = ΣyLi(x, y)p(y | Ii), R(x) = ΣN
i=1Ri(x) (2)
where Li(x, y) is the loss associated with choosing the path x at time step i when
event y occurs, Ii is an input set to the Bayesian network at time step i, and R(x) is
the total risk associated with choosing the path x, respectively. The following sections
demonstrate the risk assessment tool for AVs and UAVs.
3.1 Risk Assessment for AVs
The purpose of this tool is to calculate the risk associated with each traversal given
current knowledge of the path and the environment, and then selecting the path with
minimum risk. In this paper, traversal refers to traveling along a path at a particular time
of the day and day of the week. If the vehicle is able to compute the risk of traveling
down a path, then it might avoid risky situations and improve safety. In order to use
equation 2 for a particular traversal, two elements are used: 1) a Bayesian network that
estimates the conditional probability, p(y), of an accident and 2) a loss function for
assessing the cost of an accident.
Bayesian Network A Bayesian network was designed to calculate the probability of an
accident. Unfortunately, to the best of our knowledge, the data necessary to construct an
accurate Bayesian network related to AV accidents has not been published. Ideally, the
network would be based on data collected from an AV or, perhaps, from high-fidelity
simulations. We have thus designed a simple yet reasonable network to test the viability
of our risk assessment tool.
The Bayesian network is depicted in Fig. 1(a). Intuitively, the presence of pedes-
trians and the driving conditions play an important role in car accidents. This factor is
quantified as the number of pedestrians around the vehicle. Some driving conditions that
cause car accidents are weather, weekday vs weekend, and time of day. We assume that
these factors are independent. Night is a Boolean variable. Numberofpedestrians,
Weekday and Weather were arbitrarily categorized into four different states as shown

5. Don’t go that way! Risk-aware Decision Making for Autonomous Vehicles 5
Fig. 1. (a) A Bayesian network that attempts to capture the probability of an AV accident. All
probabilities were chosen arbitrarily. (b) A Bayesian network that attempts to capture the proba-
bility of two different types of UAV accidents: a mid-air collision (U) and the terrain impact (F).
The parent nodes are communication failure (C), communication duration failure (CD), rainy
weather (R) and engine failure (E).
in Fig.1(a). The unconditional probabilities and conditional probabilities were chosen
arbitrarily due to lack of actual data related to autonomous accidents. As the number of
autonomous vehicles increases, we expect more accident data to become available.
Loss Function The loss associated with choosing the path x at time step i when the
car accident (event y) occurs is computed as:
Li(x, y) = Qi × W1 (3)
where Qi is the total number of pedestrians around the vehicle at time step i, and W1
is the constant value assigned to a loss of life, $10,000,000 (based on recommended
insurance coverage of company vehicles). Since the loss function is dependent on the
number of pedestrians which varies by the time of travel, the loss function for the AVs
is time-varying. We use dollars as the unit of measure for the loss function in order to
make it relatable to other kinds of losses such as property losses, etc. Although it may
seem peculiar to evaluate risk in terms of dollar costs, this is standard practice in the
risk management literature [6].
By integrating the conditional probabilities with the loss function, risk is calculated
using equation 2 as:
Ri(x) = (Qi × W1) × p(Accident | Ii), R(x) = ΣN
i=1Ri(x) (4)
where N is the number of time steps for path x, Ii is the set of inputs to the Bayesian
network model at time step i, Ri(x) is the risk associated with choosing path x at time
step i and R(x) is the total amount of risk associated with choosing path x. Note that
knowledge of the current environment is assumed at the initial time i0 of evaluation,
and that to evaluate Ri(x) in the future requires a conditional probability assessment
of the likelihood of Ii taking on one of many values given known current value Ii0
for
anytime i.

6. 6 K. Mokhtari et al.
3.2 Risk Assessment for UAVs
In order to evaluate the generality of this approach, this section demonstrates that the
same method can also be used to assess the risk of a UAV’s flight path. This section
considers the possibility of a UAV using risk equation 2 to evaluate different UAV flight
paths. Here we assume that two UAVs travel along a path while communicating with
each other. The starting point of the flight is Ronald Reagan International Airport (DCA)
in Washington DC and the end point is Baltimore/Washington International Airport
(BWI). In order to employ risk equation 2, a Bayesian network is used to predict the
likelihood of two different types of accidents and a loss function is generated based on
the population density of the terrain flown over.
Bayesian Network The Bayesian network for calculating an accident is depicted in
Fig. 1. Two types of accidents are modeled (events y): mid-air collisions denoted by
U and terrain impacts denoted by F. The node C represents the communication link
between the two UAVs. If this communication link disconnects for more than a certain
threshold of time (in seconds), this results in a communication duration failure indicated
by CD. The node R captures the presence or absence of inclement weather, which im-
pacts the likelihood of both types of accidents. The node E represents an engine failure.
The nodes E, CD, and R all influence the likelihood of at least one type of accident
occurring. All the parent nodes in Fig. 1 are Boolean variables. It is assumed that CD,
R and E are independent. The node F is influenced by CD, R and E. The node U,
on the other hand, is influenced only by CD and R. A UAV dynamics model was pro-
vided by Verus Research to calculate the value of the states CD, U and F given inputs
for C, R and E. In order to apply equation 2, the conditional probability of the possi-
ble failure events given the inputs to the Bayesian network, p(U = T | CD, R) and
p(F = T | CD, R, E), must be calculated (T refers to True). The values of the proba-
bilities C, R and E are set arbitrarily as 0.4, 0.5 and 0.01, respectively. The dynamics
model is run as a part of a Monte Carlo simulation 18K times. During each run, differ-
ent inputs are presented to the dynamic model while the resulting states of the outputs
are recorded. In order to generate the Conditional Probability Tables (CPTs), the joint
probabilities for different combinations of variables must be computed. For example,
p(U = T | CD = T, R = T, E = T) is calculated as:
p(U = T, CD = T, R = T, E = T)
p(CD = T, R = T, E = T)
(5)
The same procedure is followed for every combination of inputs to create the CPTs
for events U and F.
Loss Function The loss associated with choosing a path x at time step i when U or
F occurs is Li(x, U) and Li(x, F). These losses are calculated based on the price of
the UAVs and the estimated price of the structures over which the UAVs will be flying.
Because the path is discretized into N time steps, at each time step an arbitrary cost
value denoted by W2 is assigned to the loss function depending on whether the UAVs
are flying over rural or urban areas. In the case of a terrain impact, this value is added to

7. Don’t go that way! Risk-aware Decision Making for Autonomous Vehicles 7
Fig. 2. (a) Three different paths between the same location in State College, PA. ”Path A” (left),
”Path B” (middle) and ”Path C” (right). The car traversed each path for 42 times including 7 days
and six timeslots a day: 8:45, 10:45, 12:45, 14:45, 16:45 and 17:45. Best viewed in color. (b)
Path-i (first row on the left), Path-ii (first row in middle), Path-iii (first row on the right), Path-iv
(second row on the left) and Path-v (second row on the right). Best viewed in color.
the price of the UAV denoted by PriceUAV to form the loss function. We assume that
when a mid-air collision occurs the two UAVs are completely destroyed. As a result,
the loss functions are calculated as:
Li(x, U) = 2 × PriceUAV , Li(x, F) = W2 + PriceUAV (6)
We have chosen an arbitrary cost for PriceUAV as $500,000 and W2 is considered
as $1,000,000 or $250,000 when flying over rural or urban areas, respectively. In this
case, the loss functions are time-invariant. Therefore, Li(x, U) is $1,000,000. Li(x, F)
is $1,500,000 in case of flying over urban area and is $750,000 in case of flying over
rural area.
Using the Bayesian network and the loss equations, equation 2 for a UAV becomes:
R(x) = ΣN
i=1(2 × PriceUAV ) × p(U | Ii) + (W2 + PriceUAV ) × p(F | Ii)
, R(x) = ΣN
i=1Ri(x)
(7)
where N is the number of time steps for path x, Ii is the input to the Bayesian network
at time step i and R(xi) is the risk associated with choosing path x at time step i and
R(x) is the total amount of risk associated with choosing path x. In the section that
follows we present experiments evaluating the use of this risk assessment calculation
by an AV or UAV to select a path and avoid high risk situations.
4 Experiments
This section examines the possibility of using the risk assessment methods described
above to evaluate the robot’s options, select a low risk option, and, most importantly
avoid high-risk options. We hypothesized that the methods outlined above would allow

8. 8 K. Mokhtari et al.
the robot to identify options that are particularly risky. This method is demonstrated on
both autonomous ground vehicles using real perceptual data and UAVs using simulated
data.
4.1 Experiment involving AVs
One hundred and twenty-six traversals were selected from the Pedestrian Pattern Dataset
[13]. This dataset was created by collecting video camera data from a moving vehicle
while repeatedly driving in State College, PA along the three different paths shown in
Fig. 2(a). Data collection was carried out over three weeks at times: 8:45, 10:45, 12:45,
14:45, 16:45 and 17:45 resulting in 126 distinct video samples. Each sample contains a
full HD video and GPS data for the entire traversal. By applying a Fast R-CNN based
pedestrian detection algorithm on the captured videos, the estimated number of pedes-
trians per frame is generated. As a result, the measure vi ∈ RNi×1
is generated for each
video, where Ni is the length of the i-th video in seconds (s), and each row represents
the average number of detected pedestrians at that corresponding time step. In order to
calculate the risk of traveling a path, each traversal is discretized into Ni time steps. The
Night, Weekday and Weather inputs to the Bayesian network were constant at each
time step for each traversal. The input for the Numberofpedestrians state varied at
each time step and was computed using vi. Risk for each traversal is calculated using
equation 4.
We consider a scenario as described below in which the vehicle can choose either
the path, the day, or the time of day to travel. We therefore fixed two of these variables
and used the method described in Section 3.1 to assess the risk over the remaining
options.
Which path to travel? For this study the AV must choose a path (labeled A, B, or C)
for a fixed day and fixed time. Leave-one-out cross validation was used where the data
from a particular day and time for all three paths was left out. We conducted this study
by following the steps below:
1. A day and time was selected.
2. The risk data for the three paths for this day and time was removed from the dataset
and served as ground truth.
3. The risk for the three different paths was calculated by averaging the risk for all the
remaining times and days.
4. The minimum risk path was selected as the robot’s best choice.
5. This choice was then compared to the data removed in step (2) to determine if the
selected path was actually the lowest risk option.
It should be noted that the data was real-world data which contained real natural
variations in the number of pedestrians and other factors. This process was repeated 42
times (six times a day times seven days of the week). The correct number of predictions
were divided by 42 to calculate the prediction accuracy.

9. Don’t go that way! Risk-aware Decision Making for Autonomous Vehicles 9
Which day to travel? A similar procedure was followed to select the day to travel. In
this case, the path and the time to travel were fixed and the vehicle chooses the day that
is the minimum risk option. To predict the risk for each day, the risk was averaged with
respect to path and time of day. The day predicted to be least risky was chosen. This
procedure was followed for all 18 combinations of path and time of day.
Which time to travel? Again a similar procedure was followed to determine what time
of day to travel. Here, the path and the day to travel were fixed and the vehicle was free
to choose a time to travel that minimized risk. Six different times were considered. To
predict the risk for each time, the risk was averaged with respect to path and day of
week. The procedure described above was followed for all 21 combinations of paths
and days.
4.2 Experiment involving UAVs
The purpose of this experiment is to demonstrate that the risk assessment methods de-
veloped in Section 3 are applicable to other types of vehicles, such as UAVs. We thus
assume a situation in which two UAVs travel from Ronald Reagan International Air-
port (DCA) to Baltimore/Washington International Airport (BWI) along the same path
together while communicating with each other. Both UAVs traveled along a series of
straight lines (Fig. 2(b)) for five different paths labeled Paths i-v. Paths i and Path v
mostly include urban areas. Path v mainly traverses rural areas. Each path was dis-
cretized into N time steps and the states for C, R and E were generated using a Monte
Carlo simulation at each time step. Using the Bayesian network, the conditional prob-
abilities of the mid-air collision and the terrain impact given the inputs are calculated.
By integrating these conditional probabilities with the loss functions, risk for each path
is calculated using equation 7. The results for these experiments are presented in the
next section.
5 Results and Discussion
5.1 Risk Assessment for an Autonomous Vehicle
The risk for each traversal was normalized by dividing by the sum of the risk for all the
traversals for the corresponding path. The normalized risk for all the traversals for each
path is depicted in Fig. 3. The majority of which (94.5%) are below 0.05 in normalized
risk. Tuesday at 14:45 results in a large jump in normalized risk which is five times
the average value for Path A. For Path B, only Wednesday at 14:45 is above 0.05. The
value for this path on this day is four times greater than the average for Path B. For
Path C, Friday at 10:45 is four times greater than the average for Path C. This data
clearly demonstrates that specific days and times generate spikes in risk. These spikes
are directly related to normal increases in student, pedestrian traffic during the morning
and afternoon on weekdays. We also see that some paths, such as Path A, have more
spikes in risk than the other paths. Finally, we see an overall drop in most risk at most
times during the weekend.

10. 10 K. Mokhtari et al.
Fig. 3. Normalized risk of the all traversals for each path. The spikes imply the important events
which the AV might avoid these traversals to enhance safety. Best viewed in color.
The prediction accuracy for the three prediction tasks described in Section 4.1 is
presented in Table 1. When tasked with choosing the least risky path, using the method
described in Section 4.1, the prediction accuracy was 95%. This high level of accuracy
reflects the fact that one path tends to avoid pedestrian traffic resulting in lower overall
normalized risk. When tasked with choosing the least risky day, the prediction accuracy
was 84%. Here again, there seems to be a clear risk reduction advantage to driving on
Saturday or Sunday. Finally, when choosing the least risky time, the prediction accuracy
was only 19%. This prediction accuracy reflects the fact that there is not a great risk
reduction advantage over the range of times the data was collected 8:45-17:45. If data
had been collected either later in the night or earlier in the morning, the prediction
accuracy would have likely increased because early morning hours would have resulted
in few pedestrians.
5.2 Risk Assessment for a UAV
To compare the risk for the five different paths, the computed risk for the paths was
normalized by dividing by the sum of the risk of all the paths. The normalized risk is
shown in Fig. 4. Since Path i has mainly consisted of urban areas, it has the highest
normalized risk. Path iv, on the other hand, has the lowest risk since it mostly flies over
rural areas. Overall, Paths iv and v result in similar normalized risk. Path i stands out as
a particularly risky option for the UAVs.
The loss function for the autonomous vehicle is based on the number of pedestrians
around the vehicle while the loss function for the UAVs is based on the type of area
the vehicles fly over. For the UAVs the loss can therefore be calculated a priori. For the
autonomous vehicle the loss varies with each time step and must be estimated based on
the predicted number of pedestrians. Nevertheless, in both cases certain options stand
out as entailing greater risk. The greater than average expected risk may therefore serve

11. Don’t go that way! Risk-aware Decision Making for Autonomous Vehicles 11
Fig. 4. Normalized risk for each path of the
UAVs.
Prediction Accuracy
Which Path? 95%
Which Day? 84%
Which Time? 19%
Table 1. The prediction accuracy for
the AV’s experiments.
as a signal to a UAV planning a path or an autonomous vehicle choosing a day to deliver
a package.
6 Conclusion
This paper has presented a method for an autonomous system, including an AV operat-
ing near a college campus and a pair of UAVs flying from Washington DC to Baltimore,
to evaluate the risk associated with different options and to select the minimal risk op-
tion in the hope of improving safety. We have shown that some options may offer much
greater risk than the average option. For both of these demonstrations we have made
arbitrary assumptions about the value of loss incurred should an accident occur. Be-
cause action selection is based on a comparison across available options, so long as the
same loss values are used throughout, then these assumptions do not impact the vehi-
cle’s decision making. In fact, loss can be individualized to reflect the values held by
actors or a different set of laws. For instance, one autonomous vehicle manufacturer
may place more value on accidents resulting in injury of a person over the destruction
of physical property. We also made several assumptions about the input to the Bayesian
network. We argue, however, as autonomous vehicles and UAVs become more common
and data related to their operation accumulates, more accurate Bayesian networks will
be possible.
The risks for this work were computed offline in order for the vehicles to make
predictions about the risks they would likely face to aid in future planning. Our method,
however, is computationally efficient allowing for rapid recalculation of the risks should
the system choose to reconsider its options. Our future work will explore the use of this
approach in situations where risks are rapidly changing and evolving dynamically. We
believe that this work can improve safety by allowing an autonomous vehicle to one
day avoid and react to risky situations.
7 Acknowledgment
This work was supported by Air Force Office of Sponsored Research contract FA9550-
17-1-0017 and Navy STTR contract N68335-19-C-0106.

12. 12 K. Mokhtari et al.
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