A Probabilistic Neural Network-Based Road Side
Unit Prediction Scheme for Autonomous Driving
Noura Aljeri and Azzedine Boukerche
PARADISE Research Laboratory
EECS, University of Ottawa
Ottawa, Canada
nalje094@uottawa.ca,boukerch@site.uottawa.ca
Abstract—Vehicular Networks will play a leading role in the
next generation of Autonomous Driving (AD), as recent advances
in vehicular networks are a promising solution for traffic management and congestion issues, as well as lane optimization.
Wireless mobile communication in VANETs is essential for the
content delivery of local and global information for intelligent
operation decisions in autonomous driving control applications.
However, the vehicles’ high mobility and topology changes affect
the performance of traditional mobility management protocols
over VANETs. Therefore, an efficient mobility management
solution that mitigates the challenges of vehicles’ mobility is
needed to support autonomous driving. In this paper, we present
an efficient probabilistic neural network-based Road Side Unit
(RSU) prediction scheme for autonomous driving control using
vehicular networks. We evaluate the performance of the predictor
against different machine learning models. Our results showed
a high accuracy rate in comparison to several neural network
models in various mobility environments.
I. I NTRODUCTION
In recent years, most automotive companies have moved
toward the development of new intelligent vehicles that offer
autonomous driving control features. That includes adaptive cruise control, emergency braking, navigation, collision
warning systems, automatic parking, and more [7]. Thereby,
providing safety on road and efficient traffic management
solutions. Nevertheless, several challenges arise when dealing
with autonomous driving [9], such as localization, surrounding
sensing, and most importantly reliable communication. These
challenges require accurate and efficient communication between vehicles and vehicles to roadside units to obtain reliable
information for decision making. Vehicular networks provide
the basic building block of such communications by connecting vehicles, infrastructures, and any device together [8].
The development of IEEE 802.11p and numerous routing protocols attempt to handle the rapidly shifting network
topology and the high mobility of vehicles [14]. Nonetheless,
vehicles’ connectivity through RSUs frequently changes, due
to the higher speeds and varying network topologies within
vehicular networks. Thereby providing vehicles with reliable
and seamless services is a difficult problem.
Researchers proposed several solutions to cope with the
dynamically changing topology and high mobility of vehicles
in VANETs, by integrating different type of wireless access
networks, to provide reliable and QoS services for autonomous
vehicles. However, traditional wireless access networks such
as IP, LTE, and DSRC require periodic communication updates
and registration with RSUs equipped with Access Routers
(ARs). Hence, yield high latency and data delivery disruption during transitions between RSUs. Therefore, autonomous
driving control will face difficulty in providing satisfactory
services to drivers and safety applications.
The benefits of using wireless networks can be improved by
proactively estimating the vehicles’ trajectory and upcoming
transitions early enough to initiate registration and allocating
resources ahead of time. Consequently, providing a seamless
transition and low latency in the process. Several prediction
methods can be applied to estimate the vehicle trajectory and
transition such as probability analysis [17] and movement
estimation [11]. Where the latter predict future locations by
using temporal data of the vehicles’ current movement, yet
it yields low accuracy with similar probabilities and adds
complexity and overhead to the network model. Whereas,
probability analysis deal with the probability relationship
between variables and potential ARs, which suffers from low
accuracy when sudden movement changes and misleading
behavior occur. Machine Learning (ML) is another solution
to predict the vehicles’ mobility, where various data can
be collected from the network, to derive a prediction. Two
types of ML can be used depending on a given problem:
supervised learning (classification and regression), and unsupervised learning (clustering and rules extraction) [22].
Since the vehicles’ mobility measurements, such as location,
speed, direction, and current AR, can be extracted from the
communication packets and the access router identification is
available, a supervised classification learning mechanism could
be easily adapted for such prediction purposes. The goal of
the classification task is to develop a model from a set of
observations capable of predicting the next AR (i.e., Class) of
a given unseen dataset.
In this paper, we focus on enhancing the performance of
vehicular networks connectivity by predicting the vehicles’ upcoming AR connection. Thereby, providing seamless mobility
for Autonomous Driving Control, through low computational
time and high prediction accuracy. For this purpose, artificial
neural-based network solutions are preferred over movement
estimation models [23]. However, each neural network model
Fig. 1: Mobility Management in connected cars: illustrative
example
has its strengths and weaknesses, depending on the characteristics of the data set. In this work, we adopt a Probabilistic
Neural Networks (PNNs) classification model to estimate the
next most probable AR transition given the vehicles’ mobility
characteristics. In general, PNNs classification is very much
faster than the well-known backpropagation paradigm. Besides, the output of PNNs provides not only a binary decision
of the AR class, and yet a probability matrix of all classes.
Therefore, it will be effective in case of false prediction,
in which the next most probable AR class will be used as
a fallback solution. Furthermore, we evaluate the accuracy
and computational time of PNNs in comparison to several
neural network models in forecasting the vehicles’ next AR
connection.
The remainder of this paper is organized as follows. In
Section II, we review current research work toward predicting
vehicles mobility. In Section III, we present the problem statement and preliminaries. Followed by a detailed description of
the PNN model in Section IV. The simulation experiments
and performance evaluation in Section V. The conclusion and
future directions are discussed in Section VI.
II. R ELATED W ORK
In general, we can classify mobility prediction into three
categories: probability analysis, movement estimation, and
pattern matching. Magnano et al. [17] proposed a predictive solution for handover management in VANETs, which
combines both probability analysis and movement estimation
solutions. The proposed model uses a Kalman filter to track
the vehicles’ movement, and a hidden Markov model to
predict the next access point probability. One example of
pattern matching can be found in Hu et al. [18], where partial
trajectories are matched to determine the vehicles’ future
location. The cost of comparing a vehicle’s trajectories to
an entire database is inefficient in time-sensitive applications.
Wickramasuriya et al. [12] proposed a predictive method based
on Recurrent Neural Network (RNN) to estimate the next
base station that a mobile node will connect with in 5G
wireless networks in . Their dataset composed of sequences of
Received Signal Strength (RSS) values. Each series of data is
trimmed to the last 150 RSS values before the next transition
of base stations occur. With a total of 70,000 sequences,
their results showed 98% accuracy for an 8-class sequence.
Fazio et al. [13] proposed a pattern prediction algorithm for
handover management based on Markov theory. Where they
combine a distributed hidden finite state Markov chain and
the vehicles’ traveling path to estimate the next potential AP.
However, false predictions are possible when two vehicles
share the same path and yet connect to two different APs.
Pazzi et al. proposed a similar approach in [20], using the
vehicles’ location history within AP coverage and a hidden
Markov model to estimate the next possible AP. Sadiq et
al. [21] introduced an intelligent network selection schema
to improve the prediction of the next possible AP by using
three parameters: Signal-to-Noise Ratio (SNR), Residual channel capacity, and connection lifetime. Thus, the connection
lifetime is measured according to the vehicles’ movement
estimation, identifying the lifetime between each vehicle and
an AP. However, their approach did not consider changes
in vehicles’ movement and speed. Several works have been
done toward predicting user positions and handover in Mobile
Networks. A machine-learning based handover prediction is
proposed in [16] for Wifi networks. Feltrin et al. used the
received signal strength indicator. In [15], Theodoros et al.
provided a performance evaluation of supervised learning
based location prediction scheme and validated their results
with a set of real-world and synthetic user location trajectories.
However, their results were limited to pedestrians movement
and not vehicular networks mobility.
III. P RELIMINARIES AND P ROBLEM S TATEMENT
Ubiquitous connectivity is a critical requirement for autonomous driving. Autonomous cars must be connected in
order to be smart [19]. Connectivity will enhance contextual
awareness, which will, in turn, improve the performance of
autonomous driving. In this regard, infrastructure wireless
networks will be essential for providing high coverage and
reliable communication for autonomous driving applications.
However, due to the high vehicular mobility, the performance
of traditional mobility management protocols over VANETs
will be affected, which will have further implication on
time-sensitive applications. In this section, we introduce the
problem and the interrelated assumptions in detail.
Mobility handover process relates to the maintenance of
a mobile vehicle node (VN) connection while it switches
between two points of access. When a VN establishes a
connection with a new foreign agent network (nFA), communication with its previous agent (oFA) will be distributive. VNs
will then initiate the discovery phase of finding an alternative
network by either listening for FA broadcast messages or a
vehicle sending a solicitation message. After the FA discovery
b: Algorithm flowchart
a: Architecture
Fig. 2: Probabilistic Neural Networks
process is established, vehicles will then obtain a new Care-ofAddress (CoA) and initiate the registration phase, in which the
nFA will inform oFA about the vehicles transition and request
data packets forwarding to its region. During this process,
communications between the vehicle and any corresponding
node service (CN) is disrupted. An illustrative example of mobility management design in connected networks is presented
in Figure 1.
In wireless vehicular networks, vehicular mobility is
bounded by road lanes and speed limit cap, in which we
assume a vehicle will not change its direction and speed for
a specific λ time. In our model, we concentrate on the communication between vehicles and roadside units, where each
vehicle is equipped with onboard GPS (Global Positioning
System) receiver. Therefore, vehicles can periodically record
their geographical location. During the handover process,
vehicles are responsible for calculating their directional angle
using the location data provided by the GPS as follows
dirx = currx − prevx
diry = curry − prevy
(1)
θ = atan2(diry , dirx )
When a vehicle discovers a new AR, it initiates the registration phase, in which vehicles send a binding update packet
to its oAR through the nAR. The vehicles’ oAR will then
reply with a binding update acknowledgment packet and start
tunneling and routing data packets to the vehicles’ new AR.
Meanwhile, vehicles will keep sending location update packets
to the nAR. Along with the update packet, vehicles will also
send their corresponding location, speed, and direction. The
received updates will be processed by the AR to determine to
vehicles’ future trajectory and transition.
As aforementioned, ARs will periodically obtain vehicles
movement observations and the current AR associated with the
vehicles’ measurement. These data will be used for the training
and testing of the PNN model. Each movement observation
entry is assigned to a specific AR class from a fixed set of
classes. In the following, we describe the probabilistic neural
network model in detail.
IV. P ROBABILISTIC N EURAL N ETWORKS S CHEME
A Probabilistic Neural Network (PNN) is a feedforward
neural network proposed by Specht [6], which uses radial
basis function as activation functions in the hidden layer to
make a local decision. PNNs have shown promising results
for classification problems [1], [4], [5], where local minimums
problem does not affect the decision of a PNN.
The PNN is inspired by Bayesian networks [3] and Kernel Fisher discriminant analysis [2]. Given that the standard
training procedure of a PNN requires a single pass over
all the patterns in the training set. PNN has faster training
than multilayer perceptron networks. It also yields accurately
predicted target probability score, and it is generally insensitive
to outliers [4]. The architecture of a PNN is depicted in
Figure 2a. It has four layers: an input layer, pattern layer,
summation layer, and output layer. The input layer consists of
n neurons, one for each of the n input features of an input
vector x = (x1 , .., xn )T ∈ Rn . It distributes the input to
the pattern layer’s neurons. The pattern layer’s neurons are
partitioned into k sets, one for each class in the training set.
The ith pattern neuron in the k th set computes its output as
follows:
Fk,i (X) =
||X − Xk,i ||2
1
exp(−
)
2σ 2
(2πσ 2 )n/2
(2)
where xk,i ∈ Rn is the kernel’s center, and σ is the adaptable
spread parameter. When the value of the spread parameter
is close to 0, it resembles the nearest neighbor classifier of
several training samples.
The summation layer has c neurons, one for each class. It
adds the output from the pattern layer’s neurons of a given
class. Each neuron in the summation layer estimate a class
conditional probability density function via a combination of
previously computed densities:
Gk (X) =
Mk
X
wki Fk,i (X), k ∈ 1, ..., K
(3)
i=1
where Mk is the number of pattern P
neurons of class k, and wki
Mk
is a positive coefficient such that i=1
wki = 1. The output
layer classifies an input vector, x, based on the maximum
probability obtained using Bayes’ theorem.
C(X) = arg max (Gk )
(4)
1≤k≤K
Hence, the class with the highest probability is assigned 1,
and the other classes are set to 0. The spread parameter is
critical for the classification accuracy of the PNN. To find
the best value of the spread in the PNN, the parameter may
be varied from 0.1 to 2 over different mobility scenarios as
seen in Section V-C. In the context of vehicles’ mobility and
handover process in VANETs, each vehicle observation data
xi is clustered according to the next AR class value Gi =
[1, 2, 3, .., m]; Where m is the number of ARs placed in the
network. The input vectorX comprise multiple vehicle features
including location, speed, and directional angle. The dataset
will be then fed to the PNN model for training and testing as
illustrated in Figure 2b.
TABLE I: Dataset sample.
Locxt
6766.1
6753.6
6740.7
6716.4
6703.6
6690.7
6678.3
..
Locyt
3143.9
3139
3134
3124.5
3119.6
3114.5
3109.7
.. ..
Speed(m/s)
27.29
27.29
26.92
26.92
27.51
27.51
26.69
.. ..
Direction θ
E
E
E
EW
EW
W
W
.. ..
AR class
15
15
15
16
16
16
16
.. ..
V. P ERFORMANCE E VALUATION
In this section, we evaluate the performance of the probabilistic neural network model in predicting an AR class. We
first describe the datasets, mobility environment and evaluation metrics of the experiments. Then we optimize the PNN
parameters to fit our model accurately. Finally, we compare the
PNN prediction model against three different classification algorithms (Support Vector Machine (SVM), Decision Tree, and
k-Nearest Neighbor (kNN) under diverse parameter settings,
and report the most robust classifiers. Support vector machine
[24] is based on statistical learning theory, in which it adopts
the structural risk minimization principle. As for the Decision
Tree classifier, instances are classified based on feature values
Fig. 3: Sample location dataset feature for one class ARi in
Urban environment.
[24]. The kNN is based on the proximity of each instance to
other instances with similar features [25].
We evaluate our model using MATLAB R2017, on 2.4
GHz Intel Core i7 machine. The gathered datasets were
simulated in NS-2 using Ottawa City map, for both urban
and highway environments. Simulated vehicles mobility is
generated through SUMO with an average speed between 030m/s and Krauss car-following model. We deployed ARs in
both environments to maximize area coverage. Each AR have a
communication range of 250m. We first collect each vehicles’
measurements and their AR connection periodically. Then we
process the gathered data of each vehicle by transforming each
dataset to features and targets. Finally, we train the network
in an offline mode and then perform the testing on an unseen
dataset. A sample of our dataset is presented in Table I, and an
illustrative example of one AR class associated data feature is
shown in Figure 3. Our dataset contain of four input features
and one target class (i.e. AR class), in which the input features
consist of the vehicles’ location, speed and direction.
In order to evaluate the accuracy of a classifier, several
techniques can be used. One method is to split the dataset into
training and prediction. In another method, cross-validation,
which divides the dataset into equal-sized subsets and each
subset are trained on all the other subsets. The average of
multiple rounds of cross-validation is derived from giving
an estimate of the predictor accuracy. In our test, we use
the dataset splitting technique to calculate the prediction
accuracies of each classifier.
To fully utilize the neural network model, we first need to
optimize both the smoothing parameter and data features. In
Figure 4, we evaluate the influence of spread selection on PNN
for AR prediction. We choose a spreading interval between 0.1
and 1, where we illustrate the resulted accuracies in Figure 4.
We found that the best value obtained by each data division
is between 0.4 and 0.6.
The optimum set of features that represent the vehicles’
characteristic is evaluated, where the accuracy of different
feature sets is tested as seen in Figure 5. The results indicated
Fig. 4: Spread optimization with respect to data division ratio.
Fig. 7: Prediction of vehicles’ AR class.
Fig. 5: Different features accuracies for Urban dataset.
the training time of PNN, SVM, Decision Tree, and kNN with
different percentages of dataset number. The kNN and Decision Tree showed the lowest computational time in comparison
to PNN and SVM. Moreover, PNN illustrated an average of
40s in training time. A sample of the target AR classes of
both real and predicted target classes in Figure 7.
In Figure 8, we compare the performance of multiple
classifiers techniques in term of data division for both urban
and highway environments. As seen, the accuracy of PNN
outperform all other classifiers in different dataset divisions.
Moreover, it is noted that even though kNN showed lower
computational training time, it did not exhibit higher accuracy
than PNN, specifically in an Urban environment. When we
look at the accuracy of the predictors in term of mobility
environments, it is clear that higher efficiency is presented in
the highway environment than in the urban environment. This
is due to the vehicles’ mobility characteristic in a highway
environment, in which vehicles are more restricted with certain
direction for a longer period, and vehicles exhibit fewer
changes in their speed. As for urban mobility, it is more prone
to frequent vehicles’ movement changes.
VI. C ONCLUSION
Fig. 6: Comparison of different predictors training time over
the number of dataset.
that the location and direction features report the highest
prediction accuracy in comparison to the rest. This is valid
as the vehicles’ speed is not an effective feature to predict
vehicles’ movement, where different vehicles may exhibit
different speeds and yet connect to the same access router.
At this point, we have optimized the data feature set and
the neural networks parameter to fit the vehicular networks
movement characteristics better. In the next set of experiments,
we investigate the performance of multiple classifiers in term
of computational time and accuracy. In Figure 6, we dispute
Wireless connectivity is essential to provide satisfactory
services in autonomous driving. Various wireless networks
solutions aim to provide reliable communication solutions
for autonomous driving applications. Nevertheless, the performance of such wireless networks is affected by the vehicles
high mobility and rapidly changing topology. In this paper,
we proposed an Machine Learning technique to predict the
next RSU using the vehicles’ movement features. Taking into
account the high accuracy of neural network models, we adopt
the PNN classifier model for prediction. The performance of
several classifying techniques is evaluated using the highway
and urban mobility datasets. Our results showed that the PNNs
demonstrate higher accuracy in comparison to other classifiers
in predicting vehicles’ next AR connection. However, further
enhancement can be added in the future, to include temporal
a: Urban
b: Highway
Fig. 8: Comparison of different predictors accuracies.
information to the dataset features in an online machine
learning prediction model. Therefore, evaluating the wireless
network performance up close in real time systems using
realistic dataset. Finally, a hybrid model may be considered to
combine the advantages of several machine learning models.
ACKNOWLEDGMENT
This work was partially supported by NSERC, Canada
Research Chairs Program and NSERC-CREATE TRANSIT.
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