Advanced Mathematics and Numerical Modeling of IoT

Vehicle-mounted

• Radar


• Camera

Common parts

Infra

• Driving and communication
  environment
  Neighborhood vehicle


• Vehicle state

V2V

Wireless communication

module

Positioning system

Collision detection

system

AEB logic

Configuration scenario

Vehicle trajectory

prediction

(V2V)

Ego vehicle

dynamics

Collision warning index

Level 3

Level 2

Level 1

∗

∗

∗

∗

∗

Sensor based on TTC−1

Figure 1: Block diagram of analysis model.

Normal Jerk brake Partial braking Full brake

TTC (s)

TTC> 2.0s

Braking force0.0g

TTC≤ 2.0s

Braking force−0.3g

TTC≤ 1.6s

Braking force−0.6g

TTC≤ 0.7s

Braking force−1.0g

Figure 2: AEB logic.

Camera

sensor

Radar

sensor

Lane information

Relative distance

TTC-based

collision detection

Ego vehicle

Display

warning

vehicle

Collision

avoidance

system

(AEB system)

Relative speed

Relative distance

Figure 3: Block diagram of vehicle-mounted-sensor-based AEB
system.

controlwereconductedbygraftingADASwithadaptive
cruise control and realizing intervehicle communication [ 8 –
11 ]. Consequently, a presentation on the usability of the new
conceptual AEB system that employs V2V communication
was required.
Therefore, in this study, to overcome the aforemen-
tioned limitations of vehicle-mounted-sensor-based systems,
we propose a new conceptual AEB system that employs
V2V communication along with environment recognition

sensors. In addition, the usability of V2V communication was

compared with that of vehicle-mounted sensors by modifying

an existing vehicle-mounted-sensor-based AEB system to

incorporate V2V communication.

2. AEB System Design

2.1. AEB System Analysis Model.As shown inFigure 1,for

analyzing the usability of V2V communication in comparison

with that of vehicle-mounted sensors, a detailed model

including various sensors and modules, the driving and

communication environment, and vehicle dynamic charac-

teristics was required. Therefore, in this study, PreScan, a

commercial simulation code, was used for modeling wireless

communication modules, high-precision location measure-

ment systems, the driving and communication environment,

and the radar and camera sensors installed in a vehicle. In

addition, to determine the dynamic characteristics of a user

vehicle equipped with the AEB system, a full car model

with multiple degrees of freedom was generated in CarSim,

a vehicle dynamic behavior simulation software application,

and used. Finally, after interfacing PreScan and CarSim

through MATLAB/Simulink, an analysis model was built

based on a collision detection system, AEB logic, and the

setting of a given scenario.