Automotive use case: Intelligent Predictive Cruise Control (PCC)

Overview

This use case, provided by AMPERE’s partner BOSCH, serves as an example of the increasingly autonomous decision-making capabilities of advanced automotive systems. The use case consists of four components composing different applications with different execution semantics:

  • The already existing Adaptive Cruise Control (ACC) and the Powertrain Control subsystems, which implement classical real-time systems
  • The new advanced functionalities for Predictive Cruise Control (PCC), which require data-intensive dynamic programming.
  • The Traffic Sign Recognition (TSR) subsystem, which includes video processing and machine learning workloads.

 

Block diagram of the PCC use-case
Block diagram of the PCC use-case

 

Functionality

The PCC extends the scope of the ACC functionality by calculating the vehicle’s future velocity curve using data from the electronic horizon (topographical data like curvature, inclines, or speed limits), which provides information on the route ahead, extending well beyond the next bend. In combination with the PCC, the ACC functionality now not only automatically reduces the vehicle’s speed when it detects obstacles or slow-moving vehicles ahead, but also when approaching bends and reduced speed zones.

The deep learning TSR functionality (based on deep neural networks, DNNs) detects road signs and provides speed limits to the PCC, which reflect the current state of the road (e.g. considering variable speed zones or construction sites) that are not featured in the electronic horizon. Moreover, in cooperation with the Powertrain Control, the PCC improves fuel efficiency by configuring the driving strategy based on predictive data analytics and AI methods. The potential is particularly high in the case of hybrid vehicles, where fuel consumption can be reduced by up to 15%. That is, the engine management system can use the route preview to calculate how much energy the powertrain will need and control the dynamics of the internal combustion engine and/or electric motor according to the anticipated requirements. For instance, when the system identifies a segment of the route in which a hybrid vehicle will be able to recuperate more energy than it expends, it can engage the electric motor to discharge the battery before reaching this point.

Integration

The computational power and communication bandwidth required for complex systems-of-systems, such as the intelligent PCC, exceeds the capabilities of current compute nodes (mainly micro-controller Systems on Chip, SoCs) and is leading to the paradigm of so-called centralized E/E architectures that are based on a new class of computing nodes featuring powerful micro-processors and accelerators such as FPGAs and GPUs. One consequence of these centralized E/E architectures is that heterogeneous applications will be co-existing on the same HW platform, heterogeneous not only in their model of computation (ranging from classical periodic control over event-based planning to stream-based perception applications) but also in their criticality level in terms of real-time and safety requirements. Particularly, the four components of this use case have different criticality levels, known as ASIL (Automotive Safety Integrity levels) in automotive nomenclature, that need to be isolated from each other such that there is no unintended interference between them.

Ultimately, the burden of integration is shifted from the network to the ECU level and in this regard typically from the vehicle manufacturer to the supplier of the centralized vehicle computer. Catering for the various requirements of these applications causes new integration challenges that will be considered by this use case. Moreover, with the objective of demonstrating the performance capabilities of the AMPERE ecosystem for the above mentioned integration challenges, additional (synthetic) applications will be integrated on the same computing platform. These applications resemble from a performance point of view typical workloads in autonomous driving. This will allow to further stress the computing resources and better evaluate the AMPERE ecosystem.

Key performance indicators of the AMPERE ecosystem

  • Satisfy the high computation demands of PCC algorithms while guaranteeing the safety properties of the powertrain control and ACC functionalities.
  • Maintain the functional properties of the PCC when integrating further synthetic applications, to demonstrate the compositional integration capabilities of the AMPERE ecosystem.
  • Providing a reduced development effort for integrating new functionalities in an existing system, by coupling the AMPERE ecosystem with existing automotive standards and tools.