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A Driving Intention Prediction Method Basedon Hidden Markov Model for Autonomous

DrivingShiwen Liu, Kan Zheng, Senior Member, IEEE, Long Zhao, Member, IEEE, and Pingzhi Fan,

Fellow, IEEE

Abstract—In a mixed-traffic scenario where both au-tonomous vehicles and human-driving vehicles exist, atimely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficientdriving of an autonomous vehicle. In this paper, adriving intention prediction method based on HiddenMarkov Model (HMM) is proposed for autonomousvehicles. HMMs representing different driving intentionsare trained and tested with field collected data froma flyover. When training the models, either discrete orcontinuous characterization of the mobility features ofvehicles is applied. Experimental results show that theHMMs trained with the continuous characterization ofmobility features can give a higher prediction accuracywhen they are used for predicting driving intentions.Moreover, when the surrounding traffic of the vehicleis taken into account, the performances of the proposedprediction method are further improved.

Index Terms—driving intention prediction, au-tonomous driving, hidden Markov model.

I. INTRODUCTION

Traffic safety has always been one of the importantissues in human society. With the development ofautonomous driving technology, a mixed-traffic urbanenvironment is arising, in which autonomous vehicleshave to interact with human-driven vehicles [1]. Inthe interaction between autonomous and human-drivenvehicles, how to avoid traffic accidents caused byunmanned driving has become a research field of greatconcern [2]. Generally, autonomous vehicles have tomake decisions in dynamic and uncertain environ-ments. The uncertainty comes from the fact that theintention of human drivers cannot be directly measured[3]. Hence, for an unmanned vehicle, the accurateprediction of the expected behavior of other vehicles isessential to avoid the threat of traffic accidents. In thetraditional traffic scenario where only human-drivenvehicles exist, human drivers can judge the movingintentions of the surrounding vehicles according to theestablished traffic rules and their driving experience.Based on the judgment, each driver adjusts his/herdriving in real time, in order to ensure the safety andefficiency of the traffic. However, in the mixed-trafficscenario, the unmanned vehicles have to estimate thedriving intentions of the human-driven vehicles onthe road based on pre-established prediction models.For an unmanned vehicle, it can obtain the driv-ing status of another vehicle on the road based oncommunication techniques such as vehicle-to-vehicle

(V2V) and vehicle-to-infrastructure (V2I) communi-cations. Research on the communication techniquesin vehicular networks has been widely carried out[4]- [6], which can guarantee reliable communicationsamong unmanned vehicles and human-driven vehicles.With the driving status of nearby vehicles obtained viacommunications, an unmanned vehicle can apply thepre-established prediction model to predict the futuredriving intentions of the nearby vehicles.

In recent years, researchers have been working onthe recognition and prediction of driving intentionsof vehicles. For example, Bayesian decision, supportvector machine (SVM), and hidden Markov model(HMM) etc., are widely used. In [7], the authorsproposed an algorithm to predict driver’s intention withfuzzy logic and edit distance. In [8] and [9], SVMis implemented for driving intention recognition. Themodel for detecting cognitive distraction is developedusing drivers eye movements and driving performancedata. HMMs are applicable in characterizing the under-lying relationship between observations and the hiddenstates that generate the observations. The authors in[10] proposed a method of modeling driving behaviorconcerned with certain period of past movements byusing AR-HMM, in order to predict the stop probabil-ity of a vehicle. The methods developed in [11] canbe applied in ADAS to take appropriate measures inreducing accidents. The driver intention close to a roadintersection is estimated, using discrete HMMs andthe Hybrid State System (HSS) framework as basis.The driver decisions are depicted as a discrete statesystem at a higher level and the continuous vehicledynamics are depicted as a continuous state systemat a lower level in the HSS framework. The studyin [12] focuses on the scenario when vehicles mergebecause of reduction in the number of lanes on cityroads, and considers the mutual interaction betweendrivers. However, the mobility data of vehicles used inmost existing work is generated by driving simulators,which can not accurately reflect the driving conditionsof the vehicle in real traffic environment.

In our work, the traffic data is collected from vehi-cles on a real road. When predicting the driving inten-tion of a vehicle on this road, its historical movementtrajectory is first considered, and then the surroundingtraffic close to it is taken into account. Firstly, weuse the collected data to train the prediction modelbased on HMM. Then, we use the trained model to

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predict the driving intention of a given vehicle. In thispaper, the given vehicle is considered as the targetedvehicle, and the vehicles nearby are considered asthe surrounding vehicles. When a trail of historicalinformation of the targeted vehicle, or information ofboth the targeted vehicle and the surrounding vehiclesis available, the most likely future driving intention ofthe targeted vehicle can be achieved through the pro-posed method. Moreover, either discrete or continuouscharacterization of this mobility information is appliedto make the mobility features be used as observationsin HMMs. In the discrete characterization, K-meansclustering is used for discretizing the mobility dataof vehicles. In the continuous characterization, thecontinuous mobility features are modeled as Gaussianmixture models (GMMs).

The main contributions of this paper are summarizedas follows.

• A driving intention prediction method is proposedbased on HMM, which can be used to predict thefuture moving intention of a given targeted vehi-cle, when a trail of mobility features is available.

• The HMMs are trained with data collected fromthe vehicles on a real road, and can be betteradapted to the real traffic environment.

• The prediction is carried out in the case whereonly the targeted vehicle is involved in, and inthe case where both the targeted vehicle andthe surrounding vehicles are involved in. Whenthe mobility features of the surrounding vehiclesare introduced, the performances of the proposedprediction method are further improved.

The rest of this paper is organized as follows. InSection II, we propose a driving intention predictionmethod based on HMM. The experiment scenario andnumerical results are given in Section III. Section IVconcludes this paper.

II. PREDICTION OF DRIVING INTENTIONS BASEDON HMM

In this section, we propose a driving intention pre-diction method based on HMM. The vehicle whosedriving intention is required to be predicted is referredas the targeted vehicle, and the vehicles close to it arereferred as surrounding vehicles, as shown in Fig.1.The process of the proposed method is illustrated inFig.2. The trails of mobility features of vehicles are

Targeted

Vehicle

Surrounding

Vehicles

Fig. 1. The targeted vehicle and the surroundingvehicles.

obtained, and the training of HMMs can be imple-mented in one of the two approaches. On one hand,in the HMM training with the discrete characterizationof mobility features, all the trails of features are firstlyturned into observation sequences. After that, they aredivided into a training set and a test set. The vehiclesin the training set are classified into three subsets,according to the driving intentions. Finally, HMMsrepresenting different driving intentions are trained.On the other hand, in the HMM training with thecontinuous characterization of mobility features, thetraining set is firstly divided into subsets, and then thecontinuous characterization and the training of HMMsare processed at the same time. After the HMMs arewell trained with one of the two approaches, vehiclesin the test set can be used to test the models. Ineach experiment, the driving intention of one vehiclefrom the test set is predicted. The prediction accuracyis statistically measured among all experiments. Thefollowing subsections describe each step in detail.

A. Mobility features of vehicles

Information from the targeted vehicle and the sur-rounding vehicles can be used as features for HMMtraining and prediction. Firstly, the dataset providesdynamic locations of the vehicles on a selected road.For a particular vehicle, its location coordinates arerecorded every certain seconds, and a successive trailof its locations is available. Moreover, in anotherdataset, the lanes of the roads are divided into seg-mented links, and the location coordinates of eachlink are provided. Then, by preprocessing the rawdata, some types of mobility features such as velocity,acceleration of the vehicles, and the offsets betweenthe vehicles and the lanes can be obtained. Finally,several types of mobility features are selected and usedin HMM training and prediction.

In this paper, N types of mobility features areselected for each vehicle in the HMM training andprediction. The trail of the n-th type of feature isdenoted as a vector, i.e.,

xn = [xn,1, ..., xn,t, ..., xn,T ], (1)

where t = 1, 2, ..., T is the index of the time step. Itis assumed that the trails of all types of features aretruncated to the same length of T time steps. Then,the set of all types of features of this vehicle can bewritten as a matrix, i.e.,

X = [xT1 , ...,x

Tn, ...,x

TN ], (2)

where n= 1, 2,...,N .Note that X defined above is a matrix representing

for a set of features for one vehicle. Generally, tomake an HMM converged in the training, an ade-quate number of samples are required. Assume thatL vehicles are used to train an HMM, and let Xl

denote the set of features of the l-th vehicle. Thus,a matrix F = [X1; ...;Xl; ...;XL] has a dimensionof TL × N , and it is the matrix that includes themobility features of all L vehicles, namely mobility

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Data of

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Preprocessing

of data and

mobility

features

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on of mobility

features

Observation

sequences

Training set

Test set

leftO

Test set

Vehicles

changing to left

lane

Vehicles

changing to

right lane

Vehicles

changing to left

lane

right

keep

left

left

right

keep

Trails of

mobility

features

of

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Training set

HMM training with discrete

characterization of mobility features

HMM training with continuous

characterization of mobility features

HMM

training

HMM training

with continuous

characterization

of mobility

features

left( | )P O

Forward

Algorithm

Prediction

Result

rightO

keepO

right( | )P O

keep( | )P O

Fig. 2. The framework of the proposed driving intention prediction method.

feature matrix. Note that the mobility feature matrixcan include the mobility features of both the targetedvehicle and the surrounding vehicles.

B. HMM training with discrete characterization ofmobility features

The mobility features can be represented in a dis-crete form by the technique of clustering. One possibleapproach is to apply K-means clustering, which is anexclusive clustering method based on distance. It clas-sifies the sets of mobility features into K clusters viaunsupervised machine learning technique, and outputsthe index of the cluster that each set belongs to [13][14].

As discussed above, the mobility features of Lvehicles are involved, and each vehicle gives a set offeatures at each time step. Thus, there are TL sets offeatures, and each set cd is the d-th row of mobilityfeature matrix F, where d = 1, 2, ..., TL. Each cd isregarded as a data point in the N -dimensional space,and should be classified into one of the K clusters inthe space. The main idea is to minimize the sum ofdistance from the center points to the data points inthe clusters. Let µk represent for the center point ofclsuter k, the sum of distance is denoted as

J =

TL∑d=1

K∑k=1

rd,k ‖cd − µk‖, (3)

where rd,k = 1 if cd is classified into the k-th clusterand rd,k = 0 otherwise. To ensure that J is minimized,µk should meet

µk =

TL∑d=1

rd,kcd

TL∑d=1

rd,k

, (4)

where k = 1, 2, ...,K. The detailed procedure of

discrete characterization of mobility features by K-means clustering is described in Algorithm 1.

In this paper, one particular HMM λi is trained foreach type of driving intention i = 1, 2, ..., I , whereI is the number of types of driving intention. Foran HMM λ (the index i is omitted for simplicity), itincludes a set of hidden states H , a set of observationsV , state transition probabilities A = aq,p, state-observation probabilities B = bq(j), and initial stateprobabilities π = πq [15] [16]. It can be representedas

λ = H,V,A,B, π. (5)

It is assumed that there are Q possible hidden states

Algorithm 1. Discrete characterization of mobilityfeatures by K-means clustering

Input: mobility feature matrix F, number of clustersK.

Output: observation sequences of L vehicles O =O1, ..., Ol, ..., OL.

Initialization:1: Set initial values of the center points of the clus-

ters: µ1, µ2, ..., µK .Step 1:

2: For any data point cd, classify it into cluster k,if the center point of the cluskter k is the nearestone of all K center points to it.

Step 2:3: Update rd,k according to the classification result;4: Update the values of center points µk by (4).

Step 3:5: IF µk converges, denote the final classification

result as y(d) = k when rd,k = 1; The observationof each vehicle Ol is obtained by olt = y[(l−1)T+t], where olt is the element of Ol at time step t;

6: ELSE Return to Step 1.

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in the set H . The hidden states might be the insideoperations by the drivers that causes changes in theobservations.

Given an HMM, the forward probability αt(q) isdefined as the probability of observing ol1, o

l2, ..., o

lt

and the state of the Markov chain at time t being theq-th state in the H , i.e.,

αt(q) = P (ol1, ol2, ..., o

lt, st = H(q)|λ). (6)

Similalry, the backward probability βt(q) is defined as

βt(q) = P (olt+1, olt+2, ..., o

lT , st = H(q)|λ). (7)

Then, the probability of the state at time t being H(q)is

ηt(q) =αt(q)βt(p)Q∑p=1

αt(p)βt(p)

. (8)

The probability of the state at time t being H(q) andthe state at time t + 1 being H(p) can be obtained,i.e.,

ξt(q, p) =αt(q)aq,pbq(o

lt+1)βt+1(p)

Q∑q=1

Q∑p=1

αt(q)aq,pbq(olt+1)βt+1(p)

. (9)

Algorithm 2. HMM training with discrete characteri-zation of mobility features

Input: observation sequences of L vehicles O =O1, ..., Ol, ..., OL.

Output: HMM parameters A,B, π.Initialization:

1: Initial guess of A,B, π: a(0)q,p, bp(j)

(0), π(0)q ;

2: l = 1.Step 1:

3: Use Ol and the values of HMM parameters toobtain ηt(q)(l) and ξt(q, p)(l) by (13) - (14);

4: The state transition probability of sequence Ol:

a(l)q,p =

T∑t=1

ξt(q,p)(l)

T−1∑t=1

ηt(q)(l);

5: The state-observation probability of sequence Ol:

bp(j)(l) =

T∑t=1,olt=V (j)

ηt(p)(l)

T∑t=1

ηt(p)(l);

6: The initial state probability of sequence Ol: π(l)q =

η1(q)(l).Step 2:

7: Update A by a(l)q,p = 1

l

l∑i=1

a(l)q,p;

8: Update B by bp(j)(l) = 1l

l∑i=1

bp(j)(l);

9: Update π by π(l)q = 1

l

l∑i=1

π(l)q ;

10: l = l + 1.Step 3:11: IF A, B and π converge, or l = L is met, output

λ = A,B, π;12: ELSE Return to Step 1.

As discussed above, when applying K-means clus-tering, the trail of mobility features of vehicle l isturned into one an observation sequence of integers,i.e., Ol. After that, Baum-Welch algorithm is appliedin this paper for the training of HMMs. To ensurethe convergence in the training of an HMM λi, ob-servations of Li vehicles are used. For simplicity,the index i is omitted, and the input of the trainingalgorithm is denoted as a set of observations O =O1, ..., Ol, ..., OL. The parameters of the HMM areestimated in the iteration of training process. Algo-rithm 2 gives the detailed procedure of HMM trainingin the case of discrete characterization of mobilityfeatures.

C. HMM training with continuous characterization ofmobility features

The process of clustering in the discrete charac-terization turns the trails of mobility features intosequences of integers, and then the state-observationprobabilities can be written in the form of a matrix,i.e., B. However, this may cause a loss of informationin continuous data, especially when the number ofclusters is relatively small. Gaussian mixture modelcan be used as an alternative approach to characterizethe continuous mobility features in HMM training.The n-th column of F is the vector that includesthe trails of features of type n of all L vehicles,which can be denoted as fn, with the probabilitydensity function of p(fn). Then, a superposition of MGaussian distribution is used to fit p(fn), i.e.,

p(fn) =

M∑m=1

Q∑p=1

ωn,p,mp(fn|N (µn,p,m, σn,p,m)),

(10)where ωn,p,m is the weight that the p-th state ismodelled by the m-th Gaussian component for mo-bility feature of type n, µn,p,m and σn,p,m are thecorresponding mean value and standard deviation ofthe Gaussian distribution respectively. Use a set Θn =Ωn,Mn,Σn to represent the GMM parameters forthe n-th type of feature, where Ωn = ωn,p,m, Mn =µn,p,m and Σn = σn,p,m are three 3-dimensionalmatrices. Then, we denote Θ = Θ1,Θ2, ...,ΘN asthe set of GMM parameters for the completed N typesof mobility features.

In the HMM training with continuous characteriza-tion of mobility features, maximum likelihood estima-tion is applied to compute the GMM parameters. Theobjective is to find the Gaussian distributions that aremost likely to fit the probability density functions ofthe mobility features. For the n-th type of feature, itis required to find the Θn which satisfies

arg maxΘn

p(fn) = arg maxΘn

TL∏t

p(fn(t)). (11)

To obtain the required GMM parameters [17], an initialguess of Θ is set, and then the probability of the value

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Algorithm 3. HMM training with continuous charac-terization of mobility features

Input: mobility feature matrix F, number of Gaussiancomponents M .

Output: HMM parameters A, Θ, and π.Initialization:

1: Set a initial guess of A, Θ, and π.Step 1:

2: Use the values of µn,p,m, σn,p,m, and ωn,p,m inΘ to obtain γn,p,m,t by (12);

3: Update the values of µn,p,m, σn,p,m, and ωn,p,min Θ according to (13) - (14);

4: Update A and π according to Step 1 and Step 2in Algorithm 2.

Step 2:5: Return to Step 1 and repeat until convergence.

fn(t) produced by the m-th Gaussian component is

γn,p,m,t =

Q∑p=1

ωn,p,mN (fn(t)|µn,p,m, σn,p,m)

Q∑p=1

M∑j=1

ωn,p,jN (fn(t)|µn,p,j , σn,p,j)

. (12)

The corresponding mean value and standard deviationcan be achieved, i.e.,

µn,p,m =

TL∑t=1

fn(t)γn,p,m,t

TL∑t=1

γn,p,m,t

, (13)

σ2n,p,m =

TL∑t=1

(fn(t)− µn,p,m)2γn,p,m,t

TL∑t=1

γn,p,m,t

. (14)

In this case, the state-observation probabilities arerepresented by Θ instead of a matrix. Algorithm 3gives the procedure of HMM training in the continuouscharacterization of mobility features.

D. HMM prediction

After the HMMs are well trained according to theprevious subsection, they can be used to predict thedriving intention of a given vehicle. For the targetedvehicle, a historical trail of its mobility features is usedto predict its driving intention in following time steps.For example, a historical trail of features may showthat the targeted vehicle has been keeping driving inone lane, and this indicates either a trend of lane-change or a lane-keeping behavior in future. It isrequired to predict which behavior is most likely tohappen.

Besides, the historical trail of features of the sur-rounding vehicles in the same time period may also beused together for the driving intention prediction of thetargeted vehicle. In this case, the traffic environmentaround the target vehicle is considered. Since thesurrounding vehicles close to the targeted vehicle canhave an influence on the driving behaviors of the

targeted vehicle, taking the mobility features of theminto account can help the models to predict the drivingintentions of the targeted vehicle more accurately.However, adding the trails of mobility features of somesurrounding vehicles may significantly increase thenumber of features in the training of HMMs. Witha limited maximum number of iterations, the trainingalgorithms may not be well converged in the case ofa large mobility feature matrix. As a result, the pre-diction accuracy may be dropped in some experimentswhere the algorithms are not well converged.

As described above, the historical trail of the mo-bility features of the targeted vehicle, or the historicaltrails of the mobility features of the targeted vehicleand the surrounding vehicles, can be used as theobservation of the HMM. When predicting the drivingintention, the observation is used as the input of theprediction algorithm. Then, the probability that theobservation is produced by each HMM λi is calculatedby Forward Algorithm [15]. After that, the HMM thatare most likely to give this observation is selected, i.e.,

i = arg maxi

P (O|λi), (15)

where i ∈ 1, 2, 3. In this paper, i = 1 refers tothe driving intention of changing to the left lane, i =2 refers to the driving intention of changing to theright lane, and i = 3 refers to the driving intention ofkeeping on the original lane. The driving intention thati represents for is considered as the driving intentionof the targeted vehicle.

III. EXPERIMENTAL RESULTS AND ANALYSIS

A. Experiment Scenario

As shown in Fig.3(a), the experiment is imple-mented with the traffic data of the vehicles on theJianGuoMen Flyover in Beijing. It is a three-layer in-terconnected overpass, usually with high traffic densityand complicated traffic conditions. Given a dataset ofhistorical locations of the vehicles in a period of timeon the flyover, the proposed method in this paper isused to predict the driving intentions of the vehicles.To demonstrate the scenario, the dataset is importedinto ArcMap and Fig.3(b) is plotted. Each dot is anode on the flyover and the location coordinate of itis available. Each gray line is the central axis of theroad segment. The red nodes make up a selected mainroad on the flyover, which is a two-way road withseven lanes. The historical data of 3910 vehicles onthis selected road is collected. The triangles of fourdifferent colors represent the trails of four vehicles onthis road, and the time when each location is recordedis also shown in the figure. In this paper, the drivingintentions on this road are divided into three types,i.e., changing to the left lane, changing to the rightlane, and keeping on the original lane. Some of the3910 pieces of data are used to train the model, whileothers are used as a test set and gives a predictionaccuracy, which is used as criteria for evaluating theperformances of the proposed method.

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(a) JianGuoMen Flyover in Beijing.

(b) Field collected data of vehicles on the selected road.

Fig. 3. Simulation scenario: JianGuoMen Flyover andthe selected road.

In this section, the performances of the proposeddriving intention prediction method are evaluated un-der different conditions. In the experiment, a trail offeatures of a vehicle is truncated into a sequence of 9time steps, i.e., T = 9. Since each time step is a timeperiod of 0.5 seconds, a trail lasts for 4.5 seconds.A vehicle may keep in one lane or change lanes inthe 4.5 seconds. For the convenience of classification,when truncating the mobility data of the vehicles, it isensured that the lane-changing vehicles complete thelane-change behavior (i.e., move across the lane line)at the last time step of the trail. In addition, a vehicleonly changes lane once in one trail.

As illustrated in Fig.4(a), at each time step, theselected types of mobility features of the targetedvehicle are computed, i.e., the velocity v, the movingdirection θ, and the offsets from the lane lines d1, d2.The example in Fig.4(b) gives the collected raw dataof a vehicle and the values of its selected mobilityfeatures. Moreover, since the driving intention of thetargeted vehicle may be influenced by the movementof the surrounding vehicles, this paper further takes the

vd1

d2

Selected

l2

l1

l3

l4

(a) Mobility features of the targeted vehicle and the selection ofthe surrounding vehicles.

(b) An example of the field collected data and the mobility features.

Fig. 4. Selection of mobility features.

trails of mobility features of the surrounding vehiclesinto account as well. In order to make sure thatvehicles in all directions are considered, we chooseone nearest surrounding vehicle (if it exists) from eachof the 8 regions, instead of simply choosing a specificnumber of nearest vehicles. The sizes of the regionsare determined by l1 = 4 m, l2 = 6 m, l3 = 1 mand l4 = 1.5 m. In particular, the values of l1 andl2 depend on how far away the vehicle may have aninfluence on the targeted vehicle, while l3 and l4 arerelated to the safe distance between vehicles. Then, themobility features of the selected surrounding vehiclesare added to the elements of the feature matrix F.

When evaluating the performance of the prediction,the prediction time refers to the interval between thetime that we make the prediction and the time thatthe trail finishes. For example, when the predictiontime equals to 3.0 s, it means that to predict thedriving intention of the targeted vehicle at t = T ,we have to make the prediction at 3 seconds earlier.The prediction accuracy is statistically measured. Anumber of vehicles are used as a test set and the drivingintention of them are predicted. The accuracy equalsthe ratio of correct predictions.

B. Results and Analysis

Fig.5 shows the performances of the predictionmethod when different feature characterization ap-proaches with different parameters are used in HMMtraining and prediction. When the prediction is earlier,the prediction accuracy becomes lower. Moreover, inFig.5(a), when the discrete characterization of mobilityfeatures is applied, a larger number of clusters, i.e.,K, produces a higher prediction accuracy. Fig.5(b)shows that the continuous characterization of mobilityfeatures achieves better performances than the dis-

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Prediction time (s)

K=20, Q=3 K=50, Q=3

(a) Prediction accuracy when discrete characterization is appliedwith different K.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

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HMM training with continuous characterization of mobility features

HMM training with discrete characterization of mobility features

(b) Prediction accuracy when discrete characterization or continu-ous characterization is applied.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.50.76

0.78

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Prediction time (s)

M = 2, Q = 3 M = 4, Q = 3 M = 4, Q = 5 M = 6, Q = 5

(c) Prediction accuracy when continuous characterization is appliedwith different M and Q.

Fig. 5. Prediction accuracy with different characteri-zation methods and different parameters.

crete characterization. In Fig.5(c), when the continuouscharacterization of features is applied, increasing thenumber of Gaussian components M within a range canimprove the performances of driving intention predic-tion. When the number of hidden states Q becomeslarger, the prediction accuracy is improved as well.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.50.76

0.78

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Targeted vehicle only Adding relative positions of surrounding vehicles Adding the ratio of average vehicle velocity on lanes

Fig. 6. Prediction accuracy different mobility featuresinvolved.

In Fig.6, the red line with square symbols is ob-tained when only the mobility features of the targetedvehicle are used as the input of the feature characteri-zation. Then, the mobility features of the surroundingvehicles are taken into account. Since the locationsof the surrounding vehicles may limit the drivingintention of lane-changing, the relative positions of theselected surrounding vehicles are added to the featurematrix. The relative position of a surrounding vehiclerefers to the difference in location coordinates betweenthe surrounding vehicle and the targeted vehicle. As aresult, the prediction accuracy is improved when theprediction time is long. After that, since one of thereasons that a driver chooses to change lane is to get anincrease in speed, we introduce the ratio of the averagevelocity of vehicles on the adjacent lane and that ofvehicles on the current lane as a mobility featuresadded on the basis of the previous features. The resultsshow that the prediction accuracy is further improved.Note that when the prediction time is smaller than orequals to 1.5 s, the prediction accuracy in the case ofonly mobility features of the targeted vehicle involvedis a little higher, compared to the cases where boththe mobility features of the targeted vehicle and thoseof the surrounding vehicles are involved in. This isbecause in the latter case, the number of the featuresis significantly increased and the size of the mobilityfeature matrix is enlarged. As a result, in some exper-iments, Algorithm 2 or 3 may not be converged afterthe maximum number of iterations has been reached.The accuracy is a little lower in these experiments, andhence the average prediction accuracy in this case isdropped. In general, the introduction of the mobilityfeatures of the surrounding vehicles can increase theaccuracy of the prediction of the targeted vehicle.

IV. CONCLUSION

In this paper, a driving intention prediction methodin a mixed-traffic scenario is proposed based onHMM. In the method, either discrete or continuouscharacterization of the mobility features is applied,and the mobility feature matrix is turned into a set

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of observations in HMMs. With adequate samplesof observations, HMMs representing different drivingintentions are trained by the training algorithms. Afterthat, the well-trained HMMs are used to predict thedriving intention of a given targeted vehicles. TheHMMs are trained and tested with field collected datafrom a flyover. In the HMM training and prediction,either the mobility features of the targeted vehicle, orthe mobility features of the targeted vehicle and thesurrounding vehicles are involved in. The numerical re-sults show that the HMMs trained with the continuouscharacterization of mobility features can give a higherprediction accuracy when they are used for predictingdriving intentions. Adopting different parameters inthe process of mobility feature characterization givesdifferent performances in prediction. Moreover, whenthe surrounding vehicles are involved in the trainingand prediction, the influence of the surrounding trafficon the targeted vehicle is taken into account, andthe performances of the prediction method are furtherimproved.

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