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Architecture and System Support for Safety-aware Autonomous Vehicle Design


Recently, autonomous vehicle development ignited competition among car makers and technical corporations. Low-level automation cars are already commercially available. However, the high automated vehicle where the vehicle drives by itself without human monitoring is still at infancy. Such autonomous vehicles (AVs) rely on the AV system to ensure safety. The AV system consists of two key components: data centers and onboard systems. Data centers are responsible for training deep neural network models, which will be used in the onboard system. It is necessary for data centers to train models efficiently within limited periods. The AV onboard system act as human drivers, as it monitors surroundings and plans a route for the AV to drive. To ensure safety, the AV onboard system needs to make timely and appropriate driving decisions. Moreover, a safety validation stage for the onboard system is also required to guarantee AVs' safe operations.

To address the above mentioned challenges, this dissertation proposes three designs. In Chapter 2, this dissertation proposes a processing-in-memory architecture to accelerate deep neural network training stage, which reduces the data movement overhead and improves energy efficiency. In Chapter 3, this dissertation presents the safety score, a latency based safety metric, and the latency model, that represents the correlation between perception latency and surrounding obstacle distribution; then it presents the resource management scheme to optimize safety and performance. In Chapter 4, this dissertation presents Suraksha, a generalized safety validation framework that includes a set of safety metrics and driving scenarios; it also employs Suraksha to perform a case study, where the perception module is studied and safety effects can be collected and analyzed by changing selected perception parameters.

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