Prediction based activation of vehicle safety systems – A contribution to improve occupant safety by validation of pre-crash information and crash severity plus restraint strategy prediction
Zusammenfassung
The world of transportation is rapidly changing with the introduction of partial autonomy in vehicles and the race between the manufacturers to produce a fully automated passenger vehicle. In addition, to enhance driving comfort and reduce the driving workload, these automated vehicles are also visualized as an approach to reduce the majority of accidents that are caused by human errors. However, accidents do happen and there are also some likelihoods that these automated vehicles might fail. Especially in the initial introductory years, which highlights the need for passive safety systems in safeguarding the occupants. These vehicles typically employ forward-looking sensors for the perception of the surrounding environment, which presents an opportunity to use the information from these sensors to predict an upcoming inevitable crash and further estimate the passive safety action required for the predicted crash in the pre-crash phase. This work presents an approach to activate the vehicle safety systems based on the precrash prediction.
Contents
1 Introduction 1
1.1 History of vehicle safety . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Problem formulation . . . . ....
Schlagworte
- Kapitel Ausklappen | EinklappenSeiten
- I–X Titelei/Inhaltsverzeichnis I–X
- 1–7 1 Introduction 1–7
- 1.1 History of vehicle safety
- 1.2 Problem formulation
- 1.3 Thesis outline
- 8–25 2 State of the Art 8–25
- 2.1 Passive safety sensors
- 2.2 Forward-looking sensors and their challenges
- 2.2.1 Radar
- 2.2.2 Lidar
- 2.2.3 Camera
- 2.3 Trajectory planning
- 2.4 Opponent geometry estimation
- 2.5 Crash severity estimation
- 2.6 Summary
- 26–53 3 Validation of Pre-Crash Information 26–53
- 3.1 Desired functions of the validation process
- 3.2 Underlying physical principles
- 3.2.1 Electric resistance
- 3.2.2 Capacitance
- 3.2.3 Magnetism and Induction
- 3.2.4 Piezoelectric effect
- 3.2.5 Triboelectric effect
- 3.3 Proposed Validation Sensor
- 3.3.1 Principal design
- 3.3.2 Working principle
- 3.3.3 Dynamics of contact point position and its importance
- 3.4 Experimental investigation
- 3.4.1 Test details
- 3.4.2 Sensor configuration
- 3.4.3 Results and discussion
- 3.4.4 Comparison of the investigated sensors
- 3.5 Potential for improvement
- 3.5.1 Investigation of required time for airbag activation
- 3.5.2 Summary
- 54–87 4 Opponent-object Geometry: Simplification and Estimation 54–87
- 4.1 Geometry of objects in vehicle’s surrounding
- 4.2 Proposed methodology for vehicle geometry estimation
- 4.2.1 Methodology
- 4.2.2 Experimental investigation and results
- 4.3 Geometry-based prediction of the dynamic behavior of contact points
- 4.3.1 Case 1: Collision of an ego-vehicle with a circle-based object
- 4.3.2 Case 2: Collision of an ego-vehicle with a line-based object
- 4.3.3 Case 3: Collision of an ego-vehicle with another vehicle
- 4.4 Investigations of the dynamic behavior of contact points in a vehicle crash
- 4.4.1 Case 1: Collision against a circle-based object
- 4.4.2 Case 2: Collision against a line-based object
- 4.4.3 Case 3: Collision against another vehicle
- 4.5 Comparison of geometry-based prediction with the measurements from the crash test
- 4.6 Summary
- 88–122 5 Crash Severity and Restraint Strategy Prediction 88–122
- 5.1 Basic Idea
- 5.2 Background
- 5.2.1 Vehicle structure
- 5.2.2 Restraint systems
- 5.2.3 Occupant kinematics and injuries
- 5.3 Data generation
- 5.3.1 Methodology
- 5.3.2 Vehicle level simulations
- 5.3.3 Occupant level simulations
- 5.4 Crash severity and restraint strategy prediction system architecture
- 5.5 Investigation of different machine learning algorithms
- 5.5.1 Vehicle level prediction model
- 5.5.2 Occupant level prediction model
- 5.6 Algorithm for crash severity and restraint strategy prediction system
- 123–125 6 Conclusion 123–125
- 6.1 Limitations and future work
- 6.2 Contribution
- 126–162 References 126–162
