Abstract— As autonomous-vehicle-related technologies tend

to be mature, improving passengers’ experience by learning

driving styles from human drivers becomes a promising

research topic. This study aims at learning human drivers’

velocity planning strategies for driving at curvy paths (e.g.

negotiating sharp curves, turning at intersections, etc.) on

structural road. First, we identified and extracted training trips

from the latest naturalistic driving study database. Vehicle

trajectories and the disturbances caused by other vehicles were

estimated based on sensor data. Road characteristics,

environmental parameters were identified from road

information database and video clips. Then, neural network

based models were developed to fit drivers’ speed profiles under

different driving situations. Five models with different

prediction steps were trained by up to 600 driving trips. Three

error criteria were used to evaluate the performance of

proposed models. This study verified the possibility of using

human drivers’ experience to generate velocity

recommendations for different driving conditions. The

limitations of the models are also documented.

I. INTRODUCTION

Autonomous vehicle related technologies have attracted more and more attention in recent years and are expected to be the sustainable future of transportation systems from some sense [1]. These autonomic ground robots will improve the mobility and safety of all traffic participants, in the meanwhile, transportation systems’ performance will also be enhanced greatly [2].

Since 1980s [3], semi-autonomous and autonomous driving systems have been studied by researchers and engineers. During these years, self-driving vehicle’s modules (such as decision making, path planning and trajectory generation.) have been improved and tested in controlled race environment and/or real-world experiments. However, most of the existing trajectory planning algorithms just consider the geometric paths, without accounting for reasonable speed for different driving conditions simultaneously [4]. However, during real-world driving scenarios, vehicle’s speed profile has significant influences on safety and comfortability,

Xinli Geng is with the Department of Automation, University of Science

and Technology of China, working as a research assistant in University of

Nevada, Reno during this study, also working as a research assistant at Institute of Applied Technology, Chinese Academy of Sciences,

gxinli@mail.ustc.edu.cn.

Huawei Liang, Biao Yu, Maofei Zhu are with Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of

Sciences, {hwliang@iim.ac.cn, byu@hfcas.ac.cn, mfzhu@hfcas.ac.cn}.

Hao Xu is with the Department of Civil and Environmental Engineering, University of Nevada, Reno, haox@unr.edu.

especially, for curved road segments, bad weather conditions (e.g. raining, snowing, etc.) and impaired road surface (e.g. oily, snowy, wet, etc.).

From the speed management aspect, autonomous vehicles in the contest environment usually use simplified strategies. The autonomous vehicle that has won the 2007 Defense Advanced Research Projects Agency (DARPA) urban challenge, defined four different linear velocity profiles for different driving modes and context (such as distance keeping, U-turn) [5]. The environmental conditions was not be considered in the competition. For the winner of the autonomous vehicle competition organized by the Hyundai-Kia Automotive Group [6], maximum curvature, length and risk of the selected path were considered to determine the target speed. But, the proposed method neglected the influences of road characteristics (such as grade and curvature of the roadway) and weather conditions. From the perspective of safety and comfortability, these strategies are not reasonable and affordable in the actual traffic conditions. In [7], the authors proposed an indicator to estimate the influences of bad road, weather conditions on vehicle’s longitudinal control. However, road characteristics were not integrated into the model and road, weather conditions need to be perceived by extra sensors.

Since human driver has better cognitive skills in situation analysis and decision making than machine [8], it could be a valuable approach to learn human drivers’ driving strategies for the development of autonomy systems [9][10]. In [11], the authors built a very successful online speed adaptation algorithm for high-speed, off-road autonomous driving. Supervised machine learning was used to learn the bound of acceptable shock from human driver. Gu and Dolan [4] also conducted a similar study. They built a path model and a speed model to learn human drivers’ driving patterns. However, the environment was supposed to be traffic-free, and very limited naturalistic data were used. Road characteristics and weather conditions were overlooked. Generally, previous studies either did not fully consider the influences of aforementioned factors or over-simplified the complexity of driving task in real world.

This study focuses on learning human drivers’ experience of speed management for negotiating sharp curves and turning at intersections, at where the speed limits cannot be acquired from the map directly and large amount of crashes happened [12]. In this study, considerable quantity of human drivers’ driving trips were extracted from the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS) [13], which is the largest-scale naturalistic driving study undertaken. The rest of this paper is organized as

Human-Driver Speed Profile Modeling for Autonomous Vehicle’s

Velocity Strategy on Curvy Paths

Xinli Geng1, Huawei Liang2, Hao Xu3, Biao Yu2, Maofei Zhu2

2016 IEEE Intelligent Vehicles Symposium (IV)Gothenburg, Sweden, June 19-22, 2016

978-1-5090-1821-5/16/$31.00 ©2016 IEEE 755

follows. Section II introduces the naturalistic driving data and presents the method to identify interesting trips for this study based on sensor data and road characteristics. Section III illustrates the selected parameters and the proposed models. Section IV presents the performance and comparisons of the models. At last, conclusions of the study are provided and future work is summarized.

II. DATA SOURCE AND PRE-PROCESSING

A. Naturalistic Driving Data

The SHRP 2 NDS is the largest and most comprehensive NDS undertaken. Field data were collected at six different sites in United States: Tampa, Florida; Central Indiana; Durham, North Carolina; Erie County, New York; Central Pennsylvania; and Seattle, Washington. Generally, two databases were constructed by this program, NDS database and Roadway Information Database (RID). 3,147 volunteers in all age/gender groups participated in this naturalistic driving study. More than 80,017m vehicle-mileage were collected during 3 years (from 2011 to 2013). Data Acquisition System (DAS), forward radar sensor, four video cameras, accelerometers, vehicle network information logger, Geographic Positioning System (GPS) receiver and other sensors were instrumented in the testing vehicles [13]. The SHRP2 RID contains detailed road information of the six NDS sites. 40,372 km (25,076 miles) of roadway data were collected. The detailed road geometric parameters are included in RID, for example, the grade and curve radius of the road segments.

GPS information (latitude, longitudes, speed) and on-board sensor data (e.g. lateral & longitudinal acceleration, network speed, etc.) were collected by 1 Hertz and 10 Hertz frequency, respectively. Radar sensor is able to trace 8 targets in front the testing vehicle. For each target, a sole non-zero target-ID was assigned. Targets’ relative distance and relative speed with the testing vehicle were acquired by 10 Hertz. Naturalistic driving data recorded the human drivers’ operations and driving states in various traffic conditions by time series data. In this study, only events related to curvy trajectories (turning at intersections or navigating on the curves) were identified and used by the methods introduced in the following sections. All these events were happened on structured road segments.

B. Sensor data pre-processing and interesting events

identification

In order to identify events that occurred at curvy road segments or intersections with turning actions effectively, three steps were used to estimate the movements of the vehicle based on the latitudinal acceleration and road features. First, the five-point weighted two-sides moving average filter [14] was applied to smooth the raw sensor data. The latitudinal and longitudinal acceleration, network speed at each time epoch was re-calculated by (1), where 𝑁 is the total number of the samples in one event. {𝑎𝑖 , −2 ≤ 𝑖 ≤ 2}, is weight for each corresponding nearby point, {𝑎0 = 0.333, 𝑎1 = 𝑎−1 = 0.222, 𝑎2 = 𝑎−2 = 0.111} stated in [14] was used in this study.

{𝑜𝑡 , 1 < 𝑡 < 𝑁} is the raw data point, {𝑓𝑡, 1 < 𝑡 < 𝑁} is the

filtered point. Fig. 1 shows the raw sensor data by blue stems and filtered data by red lines for different driver maneuvers.

2

2

, 2

ˆ , 2 2

, 2

t

tt j jj t

t

o t

f a o t Nt

o N t N

(1)

For some scenarios, the latitudinal acceleration kept at a constant value although the vehicle was going straight (as Fig. 1b shows). So we cannot identify drivers’ behavior by defining one or multiple thresholds directly. In order to detect the fluctuation of latitudinal acceleration, (2) was used to calculate the moving difference along the continuous time-series points by a sliding window. For 𝑖𝑡ℎ record, moving difference value (𝑚𝑑𝑖) is equal to the average of the following (𝑊𝑖 + 1) points minus the average of prior (𝑊𝑖 + 1) points. 𝑁1 and 𝑁2 are used to prevent data overflow. The window width (𝑊𝑖) was adjusted by the corresponding instantaneous speed 𝑣𝑡(3), where k is the adjustment factor and was defined as 300 in this study.

1

2

1 2

1 ˆ ˆ

min , , max ,1

N tt j jj t j N

t

t t

md f fW

N t W N N t W

(2)

1t tW kv (3)

(a) Lane keeping

(b) Left turn

0 2 4 6 8 10 12 14 16 18 20-1

-0.5

0

Time Traveled (s)

Ra

w,

Sm

oo

thed

Y-a

ccel

era

tio

n (

m/s

2)

-40

-20

0

Mo

vin

g D

effe

ren

ce,

an

d T

hre

sho

lds

Raw Data

Smoothed Data

Status

Moving Difference

Threshold 1, UB = 5

Threshold 1, LB = -5

Threshold 2, UB = 60

Threshold 2, LB = -60

0 5 10 15 20 25 30-4

-2

0

2

Time Traveled (s)

Ra

w,

Sm

oo

thed

Y-a

ccel

era

tio

n (

m/s

2)

-200

-100

0

100

Mo

vin

g D

effe

ren

ce,

an

d T

hre

sho

lds

Raw Data

Smoothed Data

Status

Moving Difference

Threshold 1, UB = 5

Threshold 1, LB = -5

Threshold 2, UB = 60

Threshold 2, LB = -60

756

(c) One right turn followed by two lane-changings

Fig. 1. Sensor data pre-processing.

Once the vehicle changes from one movement status to another one, the moving difference value will vibrate. As Fig. 1 shows, the small vibration may implicit a small latitudinal shift of the vehicle and the greater vibration may refer to the continuous turning behaviors, i.e. left-turning, right-turning. Two groups of thresholds (threshold 1, low bound (LB) is -5; threshold 1, up bound (UB) is 5; threshold 2, up bound (UB) is 60; threshold 2, low bound (LB) is -60) were defined to group moving difference into five statuses (0, ±5 and ±60) (marked by green lines in Fig. 1). In order to identify the road characteristics, road elements information in RID were combined with NDS data by GPS locations. Corresponding road characteristics (e.g. grade, curve radius, curve length, junction relationships, urban code etc.) were aggregated with each NDS data point. Finally, location types and grouped statuses were used to identify the type of events by the following rules:

Left turn/right turn: event happened at intersection and the status value changed from 0 to 60/-60.

Lane keeping: location was non-intersection and non-driveway segment, and the state kept at a consistent value.

Car following: lane keeping events without leading vehicles.

Lane changing: the state changed from -5/5 to 5/-5.

Fig. 2. Video observation tool.

After selected the interesting events by the aforementioned methods, we verified each event by observing front video clips. Finally, there were 260 events were identified for each type of behaviors, i.e. left-turn events, right-turn events, and

lane keeping/car following on curvy segments (curve radius is less than 2000m). Time-series data for each event were “cut” from the whole trip data. Weather conditions, road surface types were identified by reviewing video clips. A video observation tool was developed (Fig. 2) to extract and record these variables manually.

C. Trajectory Estimation

For the low data sampling frequency (1 Hertz) and low accuracy of GPS points, we cannot achieve the detailed trajectory of the vehicle at a fine scale, which should be used for trajectory features (such as curvature, length of the trajectory) calculation. In this study, the microscope trajectory for each selected event was estimated based on the 10 Hertz data variables (such as network speed, acceleration) according to kinematic principles.

A new coordinate system was built based on the ego-vehicle (Fig. 2). The state of the vehicle was defined as

( 𝑥𝑖 , 𝑦𝑖 , 𝜃𝑖 , 𝑑𝑖𝑥 , 𝑑𝑖

𝑦, 𝑎𝑖

𝑥 , 𝑎𝑖𝑦

, 𝑎𝑖𝑙𝑎𝑡 , 𝑎𝑖

𝑙𝑜𝑛 ), where ( 𝑥𝑖 , 𝑦𝑖 )

represents the 𝑖𝑡ℎ coordinate (named as station in this paper) in the defined coordinate system. 𝜃𝑖 is the shifting angle from

(𝑖 − 1)𝑡ℎ station to 𝑖𝑡ℎ station. (𝑑𝑖𝑥, 𝑑𝑖

𝑦) and (𝑎𝑖

𝑥, 𝑎𝑖𝑦

) are the

traveled distance and acceleration of 𝑖𝑡ℎ station. The

latitudinal acceleration (𝑎𝑖𝑙𝑎𝑡 ) and longitudinal acceleration

(𝑎𝑖𝑙𝑜𝑛) of the vehicle were collected by the sensor during NDS.

One trajectory was defined as a series of stations from the

beginning state (𝑥0, 𝑦0, 𝜃0, 𝑑0𝑥, 𝑑0

𝑦, 𝑎0

𝑥, 𝑎0𝑦

, 𝑎0𝑙𝑎𝑡 , 𝑎0

𝑙𝑜𝑛) to the

end state (𝑥𝑁 , 𝑦𝑁, 𝜃𝑁, 𝑑𝑁𝑥 , 𝑑𝑁

𝑦, 𝑎𝑁

𝑥 , 𝑎𝑁𝑦

, 𝑎𝑁𝑙𝑎𝑡 , 𝑎𝑁

𝑙𝑜𝑛), where 𝑁 is

the total number of stations for one given event.

Fig. 3. Trajectory estimation based on sensor data.

The vehicle was assumed as a mass point when travelling from current station to the next station. In the coordinate system (Fig. 3), the vehicle was located at (0, 0) at the beginning of the event. The location of 𝑖𝑡ℎ station (𝑥𝑖 , 𝑦𝑖) was calculated by (4~7). The time interval (∆𝑡) for two adjacent points is 0.1 second. The latitudinal and longitudinal velocity of 𝑖𝑡ℎ station was calculated based on the velocity and acceleration of (𝑖 − 1)𝑡ℎ station (4). Then, latitudinal and longitudinal velocity of 𝑖𝑡ℎ was used to calculate the shifting angle (𝜃𝑖). The latitudinal and longitudinal acceleration (with respect to the defined coordinate system) of 𝑖𝑡ℎ station was further be calculated by 𝜃𝑖 and the accelerations of the vehicle

( 𝑎𝑖𝑙𝑎𝑡 , 𝑎𝑖

𝑙𝑜𝑛 ). Shifting distance between 𝑖𝑡ℎ and (𝑖 − 1)𝑡ℎ

0 5 10 15 20 25 30-2

-1

0

1

2

3

Time Traveled (s)

Ra

w,

Sm

oo

thed

Y-a

ccel

era

tio

n (

m/s

2)

-20

0

20

40

60

80

Mo

vin

g D

effe

ren

ce,

an

d T

hre

sho

lds

Raw Data

Smoothed Data

Status

Moving Difference

Threshold 1, UB = 5

Threshold 1, LB = -5

Threshold 2, UB = 60

Threshold 2, LB = -60

X

Y

3xd

2xd

1xd

1y

d 2y

d3y

d

xia

yia

0xv

0y

v

00,

y lata a

0 0,x lona a

1xv

1y

v1lata

1lona

1v1

2xv

2y

v

2v2

2lata

2lona

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station can be estimated by (6). At last, the coordination of 𝑖𝑡ℎ is specified by (7), which adds up all the prior shifting

distances (𝑑1𝑥 , 𝑑2

𝑥, … , 𝑑𝑖𝑥; 𝑑1

𝑦, 𝑑2

𝑦, … , 𝑑𝑖

𝑦).

1 1

1 1

arccos( / )

x x x

i i i

y y y

i i i

x

i i i

v v a t

v v a t

v v

(4)

sin( ) cos( )

cos( ) sin( )

x lon lat

i i i i i

y lon lat

i i i i i

a a a

a a a

(5)

1 2

1 1

1 2

1 1

2 ( )

2 ( )

x x x

i i i

y y y

i i i

d v t a t

d v t a t

(6)

1

1

i x

i i

i y

i i

x d

y d

(7)

Fig. 4 shows all the trajectories for the selected events. Considerable quantity of paths were generated with regards to various road characteristics and external factors (such as weather, road surface conditions).

Fig. 4. Trajectory for selected trips.

III. PROPOSED MODEL

A. ANNs

Artificial Neural Networks (ANNs) are data-driven models, which like a “Black-box” to modeling specific patterns or systems. ANNs have the capability of modeling complex and ill-defined relationships between the input and output variables. Neural networks were considered to be robust and well-accepted models, and have been widely used in pattern classification, clustering, forecasting and optimization [15]. The ANNs models have the following advantages for observation data modeling: good fault tolerant performance for noisy and incomplete data; the capability to handle complex and non-linear problems; trained models have good prediction and generalization performance for large-scale interrelated variables.

ANNs operate like a “Black-box” system to learn the relationships between input variables ( 𝑆1, 𝑆2, … , 𝑆𝑛 ) and output parameters (𝑂1, 𝑂2, … , 𝑂𝑛) using collected data. The basic ANN architecture is comprised of three layers: input layer, hidden layer and output layer. The schematic of typical multiplayer feedback neural network architecture is shown in Fig. 5. The neurons are connected to other neurons by weighted connections. The connection weights are updated during training phase. For single neuron node, all the weighted activations from the in-coming connections are added up and transferred to an activation function. Then the outcome of this node is multiplied by corresponding weights and transferred to the next node. During the training phase, the weights and biases are adjusted to minimize the difference between outputs and desired results. Finally, the trained model can “remember” the previous observed patterns and be used to generate reasonable patterns for the new assigned input variables.

Fig. 5. The basic architecture of ANNs.

B. Variable Selection and Model Training

Before the model training, reasonable input and output variables should be defined carefully. In this study, human drivers’ driving patterns need to be learned to improve autonomous vehicles’ speed management ability for curvy paths. Although, more variables can describe the state more precisely, some factors are not easy to acquire or not appropriate for specific applications. Such as driver-related parameters, for the diversity of driving styles, it’s unpractical to combines drivers’ specific information into self-driving vehicles’ planning modules directly.

Fig. 6. Different speeds for different curvatures.

Fig. 6 illustrates the relationship between trajectory curvature and vehicle’s speed. All the trajectories shown in

.....

Input Layer Hidden Layer Output Layer

.. ...

1S

2S

nS

1O

nO

On Curves

758

Fig. 6 are traffic-free paths, in which there is no target being detected in front of the testing vehicle. The stations with the peak curvature among the corresponding event were marked by circles. It’s easy to find that when different drivers drive at stations with the same curvature, they may choose different speeds. From some sense, one driver’s style cannot represent the common patterns of all the drivers, especially, the driver’s behavior may be influenced by mood, age or other subjective factors. It will be unreasonable to learn the pattern of one specific trajectory.

Some related previous studies developed driver-specific models to learn driving patterns using one or few drivers’ short-time driving data. In this way, the results will be un-representable and cannot reflect the common pattern of different drivers. Unlike previous studies, this study tried to learn the common patterns from large-scale naturalistic driving data. The final model does not attempt to fit one individual driver, but imitate the common (“average”) patterns of quite a number of drivers.

Considering the practicability, the variables listed in Table I were used to describe driving conditions. Road features were described by five factors, i.e. grade, curve radius, speed limit, access control type, and road surface conditions. For the detailed definition of these parameters, readers can refer to the Highway Performance Monitoring System (HPMS) [16] manual. Four path features (curvature for each station, peak curvature of the path, speed of the end state, and relative longitudinal distance to the end state) were selected. In order to estimate the disturbance of other vehicles, the inverse of the distance with the nearest moving vehicle was considered as obstacle cost. For each station, nearby targets were detected by a 50-meter-radius sector area in front of the testing vehicle. The obstacle cost was equal to zero if there was no target in this area.

TABLE I. SELECTED INPUT VARIABLES

Variable Description Range

Grade The inclination angle of the road

surface to the horizontal. 0~13.5 (%)

Curve radius The radius of the curve. Smaller

curve radius indicts sharper curve. 0~2000 (m)

Speed limit Policy speed limitation. 40~ 113(m/s)

Road surface Smooth condition of the road surface. {wet, dry, snowy }

Access control Control strategies for the segments. {full, partial, no}

Weather Weather conditions that would affect

driving.

{good, raining,

snowing}

End speed The speed of the last station. 40~ 113(m/s)

Distance to the end station

The longitudinal distance to the end station.

50~300 (m)

Curvature Curvature of the station on the path. 0~1 (1/m)

Peak curvature The maximum curvature of the

trajectory. 0~1 (1/m)

Obstacle cost 1 divides the distance with the

nearest moving obstacles. 0 ~ ∞

Pre-speed The speed of the 𝑇𝑡ℎ prior station. 40~ 113(m/s)

Due to the fact that the speed of one specific station is

strongly related to the previous speeds, 𝑇𝑡ℎ prior speed

(defined as prediction length 𝑇𝑙𝑒𝑛 in this paper) was used as

one input parameter. 𝑇𝑙𝑒𝑛 decides the prediction length of the

final models. Once a short 𝑇𝑙𝑒𝑛is used, the model can just

predict the speeds for a short-term following time epochs.

The practicability of the model will be decreased. In this

study, five prediction steps(𝑇𝑙𝑒𝑛) were applied to develop five

models and the models’ performance were tested,

respectively. A three-layer ANN with 20 hidden nodes was constructed.

Levenberg-Marquardt was used as the learning algorithm. The hyperbolic tangent sigmoid transfer function and the linear transfer function were employed as the activation function of the hidden layer and the output layer, respectively. 200 events (200*3 = 600 events totally) in each type of actions were randomly selected as training sets and the remaining 60 events (60*3 = 180 events totally) were used as testing sets.

IV. RESULTS AND DISCUSSION

Five models with difference 𝑇𝑙𝑒𝑛 were constructed and trained. Testing data were used to estimate the performance of the trained models. Three error criteria were used to calculate the error dimension between desired results (𝑦𝑖 ) and the outputs (�̂�𝑖) of the models. These three measurements, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) were defined as (8, 9, 10).

1 2

1ˆ( )

N

i iiMSE N y y

(8)

1 2

1ˆ( )

N

i iiRMSE N y y

(9)

1 1

1ˆ( )

N

i i iiMAPE N y y y

(10)

As Table II shows, for one-step ahead prediction (using 𝑖𝑡ℎ station to predict the speed of (𝑖 + 1)𝑡ℎ station) the diversity error will be considerable small. The model can fit the raw data very well. When bigger prediction steps (5, 10, 20, 50, or more) were used to predict future speeds ( (𝑖 + 5)𝑡ℎ,(𝑖 + 10)𝑡ℎ, … …), the performance of the models will decrease and all the error measurements will increase (Table II), which implicates that trained models cannot re-produce all the human-patterns precisely. This phenomenon also suggests that the instantaneous velocity has strong correlation with nearby historical speeds.

TABLE II. RESULTS OF ERRORS FOR ANN MODELS

Prediction Step

(𝑻𝒍𝒆𝒏)

Error Criteria

MSE RMSE MAPE

Model 1 1 (100ms) 0.0107 0.1033 0.0127

Model 2 5 (500ms) 0.238 0.4878 0.0595

Model 3 10 (1s) 0.7906 0.8891 0.1109

Model 4 20 (2s) 0.7958 0.8921 0.1134

Model 5 50 (5s) 6.5139 2.5522 0.3395

One specific trajectory in the testing dataset was selected to investigate these models’ performance. As Fig. 7 shows, the

759

overall trend of the speed profile can be captured by all these five models. But the models with smaller prediction step can fit the raw data very well. If the prediction step is bigger, although the general trend still can be captured, the outputs are messier and a little bit different from the raw data. So for long-term multi-step prediction, the correlation between current speed and the future speeds comes to be weaker, and the following time-series data comes to be unpredictable.

Fig. 7. Results for one individual driver.

The aforementioned discussion indicts that there is a tradeoff between the accuracy and the prediction step. The longer-term predicted data sequence has lower degree of confidence than short-term prediction. But the model still can capture the human-drivers’ general pattern up to some seconds for given scenarios, which are defined by road geometric characteristics, on-road traffic, path features and environmental conditions. In practice, appropriate prediction step should be selected according to the requirements of the specific application.

V. CONCLUSION

In this paper, environmental factors (weather, road characteristics), trajectory features, and on-road traffic conditions were first considered together to identify specific driving scenarios for reasonable speed generation. Human drivers’ speed control strategies were learned based on large amount of SHRP 2 NDS data. Neural network based models were trained by interesting events, which can fill up a considerable size of search space. Based on the performance estimation, it was concluded that one of the input parameter (prediction step) should be selected carefully in practical applications. In general, for one specific driving scenario, the proposed models can generate human-like velocities for curvy trajectories, which can be planned by higher-level modules of the autonomous vehicle. Even the longer-term prediction may not be smooth and accuracy enough, the general trend of the velocity profile still can be captured up to some seconds. Post-processing methods (such as jerk smoothing algorithms) may be applied further to make generated speed commends more executable.

The next urgent step is to apply the trained models into real autonomous vehicle systems, and verify the model’s performance in the real traffic scenarios. In order to

“remember” more comprehensive driving scenarios, more external environment factors should be considered and much more naturalistic driving data should be used.

ACKNOWLEDGMENT

This research is based on the data extracted from a FHWA (Federal Highway Administration) sponsored research project, which was conducted based on Strategic Highway Research Program 2 (SHRP 2) new safety database. The study was conducted at Center for Advanced Transportation Education and Research (CATER) of University of Nevada, Reno. This study is also supported by National Nature Science Foundation of China (Grant No. 91420104, 91120307, 91320301, 61503362).

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