Computational Verification Methods For Automotive Safety Systems On Performance Evaluation Of Automotive Active Safety Systems

Electrical Electronics Engineering

Automotive active safety systems play a critical role in ensuring vehicle safety and preventing accidents. This research focuses on computational verification methods aimed at evaluating the performance of these systems. By employing advanced simulations and analysis techniques, researchers can assess the effectiveness of automotive safety features such as collision avoidance systems, adaptive cruise control, and lane departure warning systems. These methods utilize complex algorithms and real-world data simulations to validate the functionality and reliability of these safety systems under various driving scenarios. The study also explores the integration of machine learning models for predictive analytics, enhancing the accuracy and robustness of safety system evaluations. Through comprehensive computational verification, this research contributes to the ongoing advancements in automotive safety technology, ultimately enhancing road safety for drivers and passengers alike.

TABLE OF CONTENTS

Cover page

Title page

Approval page

Dedication

Acknowledgement

Abstract

CHAPTER ONE

INTRODUCTION

Background of the project
Problem statement
Aim and objectives of the project
Limitation of the study
Scope of the study

CHAPTER TWO

LITERATURE REVIEW

Overview of an automotive safety
Review of an automotive safety system
Active safety features
Passive safety features
Conclusion

CHAPTER THREE

METHODOLOGY

Basics of vehicle Distance measurement principle
Vehicle Distance measurement based on radar systems
Distance measurement based on Ultrasonic wave
The system hardware design

CHAPTER FOUR

4.0 SOFTWARE DESIGN

Longitudinal mathematical model
The vehicle stationary in front
The vehicle braking in front
The Vehicle In Front With Constant Speed Or Accelerating
The transverse model
Program design
The main program design
MCU and millimeter-wave radar data exchange
Ultrasonic signal acquisition program

CHAPTER FIVE

Conclusion
References

CHAPTER ONE

1.0 INTRODUCTION

1.1 BACKGROUND OF THE STUDY

With the development of the national economy, the people’s standard of living got corresponding improvement, cars has been one of the indispensable traffic tools in many families. But as the number of cars increasing, the traffic accident rate due to various reasons is more and more high. Road traffic accident has brought us huge losses, has become a serious social problem in worldwide. One of the main types of accidents due to driver fatigue caused by rear-ends impact compared. In order to reduce the occurrence of such incidents. The vehicle active safety systems have been studied by Many scholars, Gladwell (2014) discusses the basic methods of distance measurement, Huang et al (2010) have studied the active safety systems for reversing and a reversing radar warning system is proposed based on the ultrasonic measuring principle, it achieved a collision when reversing. Hou has studied automotive active safety systems alarm methods and key technologies (Hou et al, 2012), Cheng has designed a vehicle active safety systems to avoid traffic accidents (Cheng et al, 2019).The author combines academic work on the basis of the above proposes radar technology-based automotive active safety systems. The vehicles can detection the distance between obstacles in real-time through this system (such as the driving vehicle stop or slow in front and side guardrail), and it also can give the warning messages to drivers to take action as soon as possible. In the meantime when the real-time detection distance has lower than the limit value, the system would automatically start braking system to avoid car accidents. Automotive active safety systems based on radar range and speed. Selection of millimeter-wave radar, ultrasonic distance measuring sensor, and use AT89S52 as the core of the control system control, eventually design the car’s active safety system.

1.2 PROBLEM STATEMENT

Road traffic accidents are a major global problem, annually causing over 1.2 million fatalities according to WHO (2013). To improve road safety, active safety systems support the driver by monitoring the vehicle and its surroundings, identifying hazardous situations and actively intervening to prevent or mitigate consequences of accidents. An active safety system development was carried that makes correct decisions, in a wide variety of traffic scenarios. In this thesis, novel methods for performance evaluation of active safety systems are presented. The system decision to intervene is commonly taken when a threat function reaches a specific threshold.

1.3 AIM AND OBJECTIVES

The main aim of this work is to discuss an Active safety systems have the potential to reduce fatality and traffic related injuries significantly

The objectives are:

To reduce the rate of highway accident occurrence
To discuss a system that must make correct decisions, in a wide variety of traffic scenarios.

To determine system robustness and performance.

1.4 LIMITATION OF THE STUDY

The dimensionality of the input state space for the threat function is in general very large making exhaustive evaluation in real vehicles expensive and time consuming. An efficient theoretical method is proposed for estimating a bound on decision timing error, i.e. the worst case performance. Sensor errors are important to evaluate as they significantly affect system performance. Camera-based sensors use computer vision techniques to interpret the surrounding world, e.g. to detect, classify and track objects.

1.5 SCOPE OF THE STUDY

This thesis considers computational methods for analysis and verification of the class of automotive safety systems which support the driver by monitoring the vehicle and its surroundings, identifying hazardous situations and actively intervening to prevent or mitigate consequences of accidents. Verification of these systems poses a major challenge, since system decisions are based on remote sensing of the surrounding environment and incorrect decisions are only rarely accepted by the driver. Thus, the system must make correct decisions, in a wide variety of traffic scenarios. There are two main contributions of this thesis. First, theoretical analysis and verification methods are presented which investigate in what scenarios, and for what sensor errors, the absence of incorrect system decisions may be guaranteed. Furthermore, methods are proposed for analyzing the frequency of incorrect decisions, including the sensitivity to sensor errors, using experimental data. The second major contribution is a novel computational framework for determining the errors of mobile computer vision systems, which is one of the most widely used sensor technologies in automotive safety systems. Augmented photo-realistic images, generated by rendering virtual objects onto a real image background, are used as input to the computer vision system to be tested. Since the objects are virtual, ground truth is readily available and varying the image content by adding different virtual objects is straightforward, making the proposed framework flexible and efficient. The framework is used for both performance evaluation and for training object classifiers