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With the perpetual advancements in the field of automated driving, the first and foremost concern with regard to the acceptance of the technology by the general public is safety assurance. Safety assurance can only be established by displaying the consistency and reliability of the technology, proving that the manufacturer has taken all possible actions to mitigate the failures that might arise from the uncertainties and randomness that are introduced into the system. Statistical quantification of risks arising from potential failures is a proven metric for safety conformity as well as to determine acceptable safety levels. This thesis focuses on determining the overall injury risk by simulation of lane-keeping failure in an automated vehicle. A highly parameterized traffic model capable of simulating the interactive behavior of the automated vehicle along with other road users in normal highway traffic conditions is described in this thesis work. The overall injury risk is then determined by simulating the parameterized traffic model based on Monte Carlo simulation methodology, such that the traffic model can simulate the failure in nearly every possible traffic scenario, in concurrence with the likelihood of each state of the individual parameters that define a traffic scenario. Quantification of the risk of automated driving functions is important for safety validation, and it also provides statistically significant insights for defining the tolerable limits for a functional failure.