Designing autonomous driving systems is particularly challenging for several reasons. These systems must always make the “right” decisions to avoid accidents, so computationally intensive machine learning, computer vision and robotic processing algorithms are used to provide the required high precision. Although computationally intensive for this purpose, it is crucial for such a mission-critical system to be able to react to hazardous situations in real time. This means that signal processing always needs to be done within the strict time limitation. In addition, the autonomous driving system needs to perform the necessary calculations within a certain power budget to avoid negatively affecting the vehicle’s range and fuel thermal efficiency. To address these challenges, the following key issues need to be addressed:
Design constraints for building autonomous driving systems.
Computing power and bottlenecks for advanced end-to-end autonomous driving systems; and
Architectures for building such systems to meet all design constraints.
In this chapter, we mainly provide a brief discussion about challenges while designing autonomous driving systems from the perspectives of functional safety, real-time computational constraints, storage limitations and power/thermal constraints. Then, we also briefly discussed several key components to design one autonomous driving system, such as the perception system, decision making and vehicle control. In this chapter, we also described several available computing resources for satisfying the autonomous driving system, including the DSP/GPU and FPGA. Finally, in this chapter, the contents of the books are summarized here to give the reader a brief overview of this book.
Challenges of Autonomous Driving Systems
Autonomous driving algorithms and Its IC Design ; Chapter : 1 ; 1-23
2023-08-10
23 pages
Article/Chapter (Book)
Electronic Resource
English
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