Self-driving autonomous vehicles (AVs) have recently gained popularity as a research topic. The safety of AVs is exceptionally important as failure in the design of an AV could lead to catastrophic consequences. AV systems are highly heterogeneous with many different and complex components, so it is difficult to perform end-to-end testing. One solution to this dilemma is to evaluate AVs using simulated racing competition. In this thesis, we present a simulated autonomous racing competition, Generalized RAcing Intelligence Competition (GRAIC). To compete in GRAIC, participants need to submit their controller files which are deployed on a racing ego-vehicle on different racemore »
This content will become publicly available on October 31, 2022
Chauffeur: Benchmark Suite for Design and End-to-End Analysis of Self-Driving Vehicles on Embedded Systems
Self-driving systems execute an ensemble of different self-driving workloads on embedded systems in an end-to-end manner, subject to functional and performance requirements. To enable exploration, optimization, and end-to-end evaluation on different embedded platforms, system designers critically need a benchmark suite that enables flexible and seamless configuration of self-driving scenarios, which realistically reflects real-world self-driving workloads’ unique characteristics. Existing CPU and GPU embedded benchmark suites typically (1) consider isolated applications, (2) are not sensor-driven, and (3) are unable to support emerging self-driving applications that simultaneously utilize CPUs and GPUs with stringent timing requirements. On the other hand, full-system self-driving simulators (e.g., AUTOWARE, APOLLO) focus on functional simulation, but lack the ability to evaluate the self-driving software stack on various embedded platforms. To address design needs, we present Chauffeur, the first open-source end-to-end benchmark suite for self-driving vehicles with configurable representative workloads. Chauffeur is easy to configure and run, enabling researchers to evaluate different platform configurations and explore alternative instantiations of the self-driving software pipeline. Chauffeur runs on diverse emerging platforms and exploits heterogeneous onboard resources. Our initial characterization of Chauffeur on different embedded platforms – NVIDIA Jetson TX2 and Drive PX2 – enables comparative evaluation of these GPU platforms in executing more »
- Award ID(s):
- 1704859
- Publication Date:
- NSF-PAR ID:
- 10297482
- Journal Name:
- ACM Transactions on Embedded Computing Systems
- Volume:
- 20
- Issue:
- 5s
- Page Range or eLocation-ID:
- 1 to 22
- ISSN:
- 1539-9087
- Sponsoring Org:
- National Science Foundation
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