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Some of the recent studies reveal that it is possible to access the in-vehicle networks and inject malicious messages to alter the behavior of the vehicle. Researchers have shown that, it is possible to hack a car’s communication network and remotely take control of brake, steering, power window systems, etc. Hence, it becomes inevitable to implement schemes that detect anomalies and prevent attacks on Controller Area Network (CAN). Our work explores the complete anomaly detection process for CAN. We cover the techniques followed, available tools and challenges at every stage. Beginning with what makes CAN protocol vulnerable, we discuss case studies about attacks on CAN with major focus on Denial of Service (DoS) attack. We analyze the pattern of normal CAN messages obtained from real vehicle, along with patterns of simulated attack data using different methods/tools.

Currently, Model Based Development (MBD) is the de-facto methodology in automotive industry. This has led to conversions of legacy code to Simulink models. Our previous work was related to implementing the C2M tool to automatically convert legacy code to Simulink models. While the tool has been implemented and deployed on few OEM pilot code-sets there were several improvement areas identified w.r.t. the generated models. One of the improvement areas identified was that the generated model used atomic blocks instead of abstracted blocks available in Simulink. E.g. the generated model used an ADD block and feedback loop to represent an integration operation instead of using an integrator block directly. This reduced the readability of the model even though the functionality was correct. Thus, as a user of the model, an engineer would like to see abstract blocks rather than atomic blocks.

Various Advanced Driver Assists Systems (ADAS) are being used today to increase safety of drivers. These systems viz. Forward Collision Warning (FCW), Lane Departure Warning (LDW), Pedestrian Detection (PD), are all based on inputs captured using a front mounted camera. It would be useful to combine all these applications together and process the same input for different application purpose. Additionally, multicore processors are now easily available and can be used for integrating multiple ADAS applications. This would lead to reduced cost and maintenance of ADAS systems with the same performance benefits. Since current ADAS applications are sequential and/or use single core processors there is a need to parallelize these applications so that multiple cores can be utilized optimally. In this paper, we discuss our experiments and results while attempting to integrate two such ADAS applications on a multicore embedded platform.