Hello, everyone, I'm Jingyang Hu from Hunan University.
It is my honor to report our work on Ubicomp twenty and twenty-one.
The title of my report is "DriverSonar: Fine-Grained Dangerous Driving Detection Using Active Sonar".
In fact, this work provides a new method for dangerous driving actions detection.
It was done by Hongbo Jiang, Daibo Liu, Jie Xiong, Minjie Cai, and me.
I would like to thank them and all volunteers for their efforts in this work.
In the last decade, a lot of important measures had been developed to enhance driving safety.
yet a significant number of severe accidents still occurred all over the world.
Traffic accidents cause economic loss. Drowsiness and distraction are the major causes of traffic accidents
It is therefore critical and urgent to develop a system which can accurately detect dangerous driving behaviors
and alert drivers in real time, preventing potential car accidents.
To detect dangerous driving actions, existing solutions have explored different methods by monitoring various driving features.
Physical features are susceptible to individual diversity and background noise and
vehicular features are vulnerable to weather and traffic conditions.
Moreover, although traditional vision-based systems can achieve a high detection accuracy,
cameras do raise severe privacy concerns, hindering the wide adoption of these systems.
Inspired by the above observations, we present DriverSonar.
The key insight of DriverSonar is that these dangerous driving motions cause unique signal
variations and we can thus exploit these distinctive signal features.
The proposed system detects dangerous driving actions and generates real-time alarms using off-the-shelf smartphones.
The Reasons for Using a Smartphone to Detect Dangerous Driving Actions.
First,Different driving actions leads to constant changes in the distance between the smartphone and the driver's head and hand.
Second, The smartphone periodically transmits chip signals and tracks the echoes
from both head and steering hand with the built-in microphone.
After Feasibility Study, we found Signal features include variation of the signal amplitude,
duration of the abnormal state is different between different actions.
The whole system consists of four modules: acoustic signal generation and pre-processing, head and
hand echo detector, fine-grained feature extraction, and dangerous driving action detection.
We set the duration of chirp as 1 ms. The interval between adjacent chirps is set to no less than 5 ms
so all echoes within the range of one meter can be captured.
When a chirp signal is transmitted from the speaker, part of the signal directly propagates to
the microphone through the line of sight path, and others are reflected by different obstructions.
To identify the echoes reflected by head and steering hand from the received signals.
DriverSonar explores the correlation between the received signal and the originally
transmitted chirp signal by computing the relative correlation coefficient.
By applying RCC and the property of motion continuity,
we can effectively separate multiple echoes and obtain the motion change of corresponding objects in time domain.
To show the distance change of objects, DriverSonar transforms the RCC-Distance profile
in time series into the change of distance between the objects and smartphone.
In the last According to these characteristics of hand and head, DriverSonar can effectively eliminate potential interference echoes in advance.
Then, the profile is transformed into a condensed version.
After Head and Hand Echoes Detection, the behavioral features in driving are exploited to identify echoes reflected from driver's head and hand.
It is rational to use the fine-grained features of driving actions involving both head and steering hand in time domain.
We use DTW-based motion change series matching algorithm to detect dangerous driving action.
Then DriverSonar adopts two different strategies to deal with distracted driving and drowsy driving respectively.
We instruct volunteers to mimic drowsy driving, such as nodding, yawning, hand off the steering wheel and looking outside for a while.
The specific details are not repeated here.
In the experiment, we evaluate the overall system performance of DriverSonar.
These results indicate that DriverSonar can reliably detect dangerous driving actions in real time and timely alert drivers when they are in dangerous driving states.
We have also done a lot of experiments on the impact of volunteers and driving conditions on the performance of driversonar.
These results indicate DriverSonar can adapt to different users and driving conditions.
we exploit the motion patterns of both head and steering hand to jointly determine dangerous driving action.
We implement the prototype of DriverSonar and conduct extensive experiments in both simulated and real driving environments.
The results show that DriverSonar can effectively detect dangerous driving actions in real-time.
Thank you for your attention. Please contact me if you have any questions.