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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.

Automobiles are getting more and more sophisticated with increased demand for more comfort and safety by customers. Due to this, the automotive Electronic Control Units (ECU) and the software applications running on these ECUs have become more complex and computationally more intensive. This has resulted in Original Equipment Manufacturers (OEMs) migrating to multicore platforms. Optimal usage of multicore platform necessitates the design of new scheduling algorithms. In the past decade, different approaches to implement hard real time scheduling in automotive domain have been proposed for single core as well as multicore architectures. We explore different scheduling techniques proposed so far which are relevant to automotive domain and also, provide a taxonomy of these scheduling algorithms, which will help the automotive design engineer to make an informed decision.

The automotive industry today follows Model Based Development (MBD) for developing modern automotive applications. This method involves creating models for a system under design and then using tools like MATLAB/Simulink to auto-generate code for target platforms. This method is popular since maintenance of MBD based applications is simple and less time consuming as compared to maintaining hand-written application code. Thus, MBD facilitates correct designs and easy maintenance of automotive applications. However, there are legacy automotive applications that are not developed using models. It is difficult to accommodate and test any changes in such application codes since it requires extensive testing. Additionally, for application code generated from models, many a times, code is changed during testing and these changes are not reflected in the model. Hence, there is a need to convert legacy automotive application codes to models.

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.

Improving reliability and quality of software is a major aspect in automotive industry. Software reliability and quality improves by reducing bugs or defects in the software. However, finding these defects at an early stage in the software development life cycle is important to reduce rework and cost. Manually detecting defects or bugs in large code sets is time consuming and is less accurate. Hence, using static or dynamic analysis tools has become a standard practice in automotive industry. Though many such tools are commercially available, it is observed that these tools are less used for various reasons. Some of the major reasons are users need to spend considerable amount of time to learn to use these tools to get desired output reports, customized checks are required for an application that are not provided by the tool and reports are too lengthy as well as cumbersome to analyze.