<p>A CMbB system is a system that with the help of sensors in the front of a car detects when a collision in unavoidable. When a situation like that is detected, the brakes are activated. The decision of whether to activate the brakes or not is taken by a piece of software called a decision maker. This software continuously checks for routes that would avoid an object in front of the car and as long as a path is found nothing is done. Volvo has been investigating several different CMbB-systems, and the research done by Volvo has previously focused on decision makers that only consider one object in front of the car. By instead taking all present objects in consideration, it should be possible to detect an imminent collision earlier. Volvo has developed some prototypes but needed help evaluating their performance. </p><p>As part of this thesis a testing method was developed. The idea was to test as many cases as possible but as the objects’ possible states increase, the number of test cases quickly becomes huge. Different ways of removing irrelevant test cases were developed and when these ideas were realized in a test bench, it showed that about 98 % of the test cases could be removed. </p><p>The test results showed that there is clearly an advantage to consider many objects if the cost of increased complexity in the decision maker is not too big. However, the risk of false alarms is high with the current decision makers and several possible improvements have therefore been suggested.</p>

A CMbB system is a system that with the help of sensors in the front of a car detects when a collision in unavoidable. When a situation like that is detected, the brakes are activated. The decision of whether to activate the brakes or not is taken by a piece of software called a decision maker. This software continuously checks for routes that would avoid an object in front of the car and as long as a path is found nothing is done. Volvo has been investigating several different CMbB-systems, and the research done by Volvo has previously focused on decision makers that only consider one object in front of the car. By instead taking all present objects in consideration, it should be possible to detect an imminent collision earlier. Volvo has developed some prototypes but needed help evaluating their performance. As part of this thesis a testing method was developed. The idea was to test as many cases as possible but as the objects’ possible states increase, the number of test cases quickly becomes huge. Different ways of removing irrelevant test cases were developed and when these ideas were realized in a test bench, it showed that about 98 % of the test cases could be removed. The test results showed that there is clearly an advantage to consider many objects if the cost of increased complexity in the decision maker is not too big. However, the risk of false alarms is high with the current decision makers and several possible improvements have therefore been suggested.

Today many car manufactures are developing systems that help the driver to avoid collisions. Examples of this kind of systems are: adaptive cruise control, collision warning and collision mitigation / avoidance. All these systems need to track and predict future positions of surrounding objects (vehicles ahead of the system host vehicle), to calculate the risk of a future collision. To validate that a prediction is correct the predictions must be correlated to observations. This is called the data association problem. If a prediction can be correlated to an observation, this observation is used for updating the tracking filter. This process maintains the low uncertainty level for the track. From the work behind this thesis, it has been found that a sequential nearest- neighbour approach for the solution of the problem to correlate an observation to a prediction can be used to find the solution to the data association problem. Since the computational power for the collision mitigation system is limited, only the most dangerous surrounding objects can be tracked and predicted. Therefore, an algorithm that classifies and selects the most critical measurements is developed. The classification into order of potential risk can be done using the measurements that come from an observed object.

<p>Today many car manufactures are developing systems that help the driver to avoid collisions. Examples of this kind of systems are: adaptive cruise control, collision warning and collision mitigation / avoidance. </p><p>All these systems need to track and predict future positions of surrounding objects (vehicles ahead of the system host vehicle), to calculate the risk of a future collision. To validate that a prediction is correct the predictions must be correlated to observations. This is called the data association problem. If a prediction can be correlated to an observation, this observation is used for updating the tracking filter. This process maintains the low uncertainty level for the track. </p><p>From the work behind this thesis, it has been found that a sequential nearest- neighbour approach for the solution of the problem to correlate an observation to a prediction can be used to find the solution to the data association problem. </p><p>Since the computational power for the collision mitigation system is limited, only the most dangerous surrounding objects can be tracked and predicted. Therefore, an algorithm that classifies and selects the most critical measurements is developed. The classification into order of potential risk can be done using the measurements that come from an observed object.</p>

This thesis work aims at performing a survey of those technologies generally called DriverAssistance Systems (DAS). This thesis work focuses on gathering information in terms ofaccident statistics, sensors and functions and analyzing this information and shall thruaccessible information match functions with accidents, functions with sensors etc.This analysis, based on accidents in United States and Sweden during the period 1998 – 2002and two truck accident studies, shows that of all accidents with fatalities or sever injuriesinvolving a heavy truck almost half are the result of a frontal impact. About one fourth of theaccidents are caused by side impact, whereas single vehicle and rear impact collisions causesaround 14 % each. Of these, about one fourth is collision with unprotected (motorcycles,mopeds, bicycles, and pedestrians) whereas around 60 % are collision with other vehicles.More than 90 % of all accidents are partly the result of driver error and about 75 % aredirectly the result of driver error. Hence there exist a great opportunity to reduce the numberof accidents by introducing DAS.In this work, an analysis of DAS shows that six of the systems discussed today have thepotential to prevent 40 – 50 % of these accidents, whereas 20 – 40 % are estimated to actuallyhaving the chance to be prevented.One of these DAS, automatic emergency brake (AEB), has been analyzed in more detail.Decision models for an emergency brake capable to mitigate rear-end accidents has beendesigned and evaluated. The results show that this model has high capabilities to mitigatecollisions.

New automated vehicles have the chance of high improvements to road safety. Nevertheless, from today's perspective, accidents will always be a part of future mobility. Following the “Vision Zero”, this thesis proposes the quantification of the driving situation's criticality as the basis to intervene by newly integrated safety systems. In the example application of trajectory planning, a continuous, real-time, risk-based criticality measure is used to consider uncertainties by collision probabilities as well as technical accident severities. As result, a smooth transition between preventative driving, collision avoidance, and collision mitigation including impact point localization is enabled and shown in fleet data analyses, simulations, and real test drives. The feasibility in automated driving is shown with currently available test equipment on the testing ground. Systematic analyses show an improvement of 20-30 % technical accident severity with respect to the underlying scenarios. That means up to one-third less injury probability for the vehicle occupants. In conclusion, predicting the risk preventively has a high chance to increase the road safety and thus to take the “Vision Zero” one step further.:Abstract Acknowledgements Contents Nomenclature 1.1 Background 1.2 Problem statement and research question 1.3 Contribution 2 Fundamentals and relatedWork 2.1 Integral safety 2.1.1 Integral applications 2.1.2 Accident Severity 2.1.2.1 Severity measures 2.1.2.2 Severity data bases 2.1.2.3 Severity estimation 2.1.3 Risk assessment in the driving process 2.1.3.1 Uncertainty consideration 2.1.3.2 Risk as a measure 2.1.3.3 Criticality measures in automated driving functions 2.2 Operational motion planning 2.2.1 Performance of a driving function 2.2.1.1 Terms related to scenarios 2.2.1.2 Evaluation and approval of an automated driving function 2.2.2 Driving function architecture 2.2.2.1 Architecture 2.2.2.2 Planner 2.2.2.3 Reference planner 2.2.3 Ethical issues 3 Risk assessment 3.1 Environment model 3.2 Risk as expected value 3.3 Collision probability and most probable collision configuration 4 Accident severity prediction 4.1 Mathematical preliminaries 4.1.1 Methodical approach 4.1.2 Output definition for pedestrian collisions 4.1.3 Output definition for vehicle collisions 4.2 Prediction models 4.2.1 Eccentric impact model 4.2.2 Centric impact model 4.2.3 Multi-body system 4.2.4 Feedforward neural network 4.2.5 Random forest regression 4.3 Parameterisation 4.3.1 Reference database 4.3.2 Training strategy 4.3.3 Model evaluation 5 Risk based motion planning 5.1 Ego vehicle dynamic 5.2 Reward function 5.3 Tuning of the driving function 5.3.1 Tuning strategy 5.3.2 Tuning scenarios 5.3.3 Tuning results 6 Evaluation of the risk based driving function 6.1 Evaluation strategy 6.2 Evaluation scenarios 6.3 Test setup and simulation environment 6.4 Subsequent risk assessment of fleet data 6.4.1 GIDAS accident database 6.4.2 Fleet data Hamburg 6.5 Uncertainty-adaptive driving 6.6 Mitigation application 6.6.1 Real test drives on proving ground 6.6.2 Driving performance in simulation 7 Conclusion and Prospects References List of Tables List of Figures A Extension to the tuning process

The aim of the project is to design an active safety system forpassenger vehicles for mitigating secondary collisions after an initialimpact. The control objective is to minimize the lateral deviationfrom the known original path while achieving a safe heading angle afterthe initial collision. A hierarchical controller structure is proposed:the higher layer is formulated as a linear time varying model predictivecontroller that denes the virtual control moment input; the lowerlayer deploys a rule-based controller that realizes the requested moment.The designed control system is then tested and validated inSimulink as well as in IPG CarMaker, a high delity vehicle dynamicssimulator. / Syftet med projektet är att för personbilar designa ett aktivtsäkerhetssystem för att undvika följdkollisioner efter en första kollision.Målet är att minimera den laterala avvikelsen från den ursprungligafärdvägen och att samtidigt uppnå en säker kurs efter den första kollisionen.En hierarkisk regulatorstruktur föreslås. Det övre skiktet iregulatorn är formulerat som en linjär tidsvarierande modell prediktivkontroller som definierar den virtuella momentinmatningen. Det nedreskiktet använder en regelbaserad regulator som realiserar det begärdamomentet. Det konstruerade styrsystemet testades och validerades sedani Simulink samt i IPG CarMaker, en simulator med hög precisionför fordonsdynamik.