The number of computer-controlled functions in an automobile is increasing at a rapid rate and so is the number of microprocessors implementing and controlling these functionalities. Therefore, there is a need to minimize the computing power provided without affecting the performance and safety of the applications. The latter is especially important since intelligent automotive applications deal with critical data and involve deadline bound computations on data gathered from the automobiles? environment. These applications have stringent requirements on the freshness of data items and completion time of the tasks. Our work studies one such safety-critical application, namely Adaptive Cruise Control (ACC). We take a task+data centric approach for designing and

As our contributions we have (i) identified the data and task characteristics of ACC and shown how to map them on a real-world (robotic) platform, (ii) facilitated a realtime approach towards designing ACC by the application of mode-change and real-time data repository concepts for reducing CPU capacity requirements and (iii) provided the scheduling strategies to meet the timing requirements of the tasks. Experiments demonstrate that the CPU capacity requirement can be reduced without compromising the real-time guarantees for safety-critical applications.