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Configuring ADAS Platforms for Automotive Applications Using Metaheuristics.

Shane D McLean¹, Emil Alexander Juul Hansen¹, Paul Pop¹

¹Technical University of Denmark Kongens Lyngby, Kongens Lyngby, Denmark.

Frontiers in Robotics and AI

|January 21, 2022

Summary

This study presents new metaheuristic algorithms for configuring Advanced Driver-Assistance Systems (ADAS). The methods efficiently schedule tasks and messages on complex automotive platforms, improving deadline satisfaction and latency constraints.

Keywords:

IEEE 802.1Qbv TSN automotive applications task preemption task scheduling time-sensitive networking

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Area of Science:

Computer Engineering
Embedded Systems
Automotive Systems

Background:

Modern Advanced Driver-Assistance Systems (ADAS) integrate real-time safety-critical tasks with best-effort tasks on multi-core, multi-System-on-Chip (SoC) platforms.
These systems feature complex interdependencies, forming end-to-end latency chains (sensing, processing, actuating) that must meet strict deadlines.
Existing scheduling methods struggle with the combinatorial complexity of configuring these increasingly sophisticated ADAS platforms.

Purpose of the Study:

To address the intractable combinatorial optimization problem of configuring ADAS platforms for automotive applications.
To develop efficient heuristics and metaheuristics for mapping tasks to cores and scheduling tasks and messages.
To ensure satisfaction of task/message deadlines and end-to-end latency chain constraints in ADAS.

Main Methods:

Proposed two metaheuristic solutions: a Genetic Algorithm (GA) and a Simulated Annealing (SA) approach for static task scheduling.
These metaheuristics simulate Earliest Deadline First (EDF) dispatching with varied task deadlines and offsets.
A List Scheduling-based heuristic was employed to generate Gate Control Lists (GCLs) for Time-Sensitive Networking (TSN) backbones.

Main Results:

The proposed heuristic strategy successfully finds feasible solutions that meet complex timing and dependency constraints.
Achieved over 6 times higher satisfaction rate for jitter constraints compared to related work.
Demonstrated a 41% average increase in satisfying task chain constraints, with good scalability for future ADAS.

Conclusions:

The developed GA and SA metaheuristics, combined with List Scheduling for GCLs, provide effective solutions for ADAS platform configuration.
The approach significantly improves the reliability of meeting critical timing requirements in complex automotive systems.
The method scales well, addressing the growing complexity and demands of next-generation ADAS.