From the VTS Resource Center: The Control of Self-Driving Cars

11 June, 2019 | TA Resource Center
Title: Impact of Environmental Information on the Control of Self-Driving Cars
 Raghvendra Cowlagi and Alexander Wyglinski, Worcester Polytechnic Institute

Description: Vehicular autonomy, ranging from driver assistance to full self-driving autonomy, and vehicle-to-everything (V2X) wireless connectivity promises to revolutionize the safety, reliability, and energy-efficiency of future automotive transportation. Connected autonomous vehicles (CAVs) are cyber-physical systems with increasingly complex software algorithms in control of a physical vehicle moving in uncertain real-world environments.

Planned connectivity regulations and recent advances in vehicular autonomy by leading manufacturers imply that CAVs will be ubiquitous in the near future. Roads will be data-rich environments where a large number of wireless devices attached to vehicles, infrastructure, personal electronics, and wearable gadgets will transmit multimodal data.

Consequently, it is important to understand the de facto upper bound on the number of data sources that can be accommodated by the autonomy algorithms as well as the limitations of the surrounding wireless environment to support the multiple communication links between vehicles and nearby road-side infrastructure. In this lecture, we will explore the intricate bidirectional interactions between the technologies of autonomy and of wireless connectivity in cyber-physical systems.

Specifically, we will study how estimation and control algorithms affect – and are affected by – software-defined radio communications in spectrum-scarce, data-rich environments. In particular, we will study how to perform dynamic information selection and how it evolves with the trajectory plan. This is based on the so-called method of lifted graphs, which promises to bridge the gap between fast geometric path planning algorithms and slower control-theoretic techniques that incorporate vehicle dynamical constraints.

Furthermore, trajectory planning approaches presented in this lecture will be extended to other formulations and solutions of different application-specific planning problems.

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