Good responsiveness of autonomous vehicle computing systems is crucial to safety and performance of the vehicles. For example, an autonomous vehicle (AV) may cause an accident if the end-to-end latency from sensing a pedestrian to emergency stop is too high. However, the AV software stacks are too complex to probabilistically analye the end-to-end latency on a multi-core system. They consist of a graph of tasks with different periods, and have a large variability in the task execution times, which may lead to the maximum core utilization Umax >= 1.0 on some cores. This paper proposes a novel stochastic analysis of the end-to-end latency over the AV stacks that allows Umax to exceed 1.0 on each core. The proposed analysis models the entire stack as a graph of task graphs under a multi-core partitioned scheduling and provides a probabilistic guarantee that the analyzed latency distribution upper-bounds the one observed from a real system under the assumption of independent task execution times. Using the Autoware stack with inter-task dependent execution times, it is shown that our analysis, combined with a task grouping to mitigate the inter-task correlations, can give a latency distribution for each task path that almost upper-bounds the observed one.

We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately to enhance the adaptation performance for ambiguous samples. To estimate the hidden intermediate domain and the unknown labels of the target domain simultaneously, we develop a training algorithm using a double-structured architecture. We validate the proposed framework in hard adaptation scenarios with real-world datasets from simple synthetic domains to complex real-world domains. The proposed algorithm outperforms the previous state-of-the-art algorithms on various environments.

When a new problem is given to be solved with high performance, deep learning needs large effort to tune the model hyperparameters including model architectures and training hyperparameters. Many previous works have tried to tune the model hyperparameters automatically, but the algorithms only target on searching either model architectures or training hyperparameters. However, simultaneous optimization of the model architectures and the training hyperparameters works slow and falls into bad local minimums because of the search space enlarged by the high correlation between the two model hyperparameters. In this paper, we propose a novel algorithm to efficiently find the best set of model architectures and training hyperparameters. To efficiently handle the large search space, the proposed algorithm selectively utilizes the given training samples, while limiting the search space by a novel ensemble sampling method. Also, the evaluation time is further reduced by a novel termination mechanism. The accelerated computation of the proposed algorithm is validated by using complex image datasets, which shows the state-of-the-art performance with the 70.9% reduction of computational time.