Configuring ADAS Platforms for Automotive Applications Using Metaheuristics

Modern Advanced Driver-Assistance Systems (ADAS) combine critical real-time and non-critical best-effort tasks and messages onto an integrated multi-core multi-SoC hardware platform. The real-time safety-critical software tasks have complex interdependencies in the form of end-to-end latency chains featuring, e.g., sensing, processing/sensor fusion, and actuating. The underlying real-time operating systems running on top of the multi-core platform use static cyclic scheduling for the software tasks, while the communication backbone is either realized through PCIe or Time-Sensitive Networking (TSN). In this paper, we address the problem of configuring ADAS platforms for automotive applications, which means deciding the mapping of tasks to processing cores and the scheduling of tasks and messages. Time-critical messages are transmitted in a scheduled manner via the timed-gate mechanism described in IEEE 802.1Qbv according to the pre-computed Gate Control List (GCL) schedule. We study the computation of the assignment of tasks to the available platform CPUs/cores, the static schedule tables for the real-time tasks, as well as the GCLs, such that task and message deadlines, as well as end-to-end task chain latencies, are satisfied. This is an intractable combinatorial optimization problem. As the ADAS platforms and applications become increasingly complex, such problems cannot be optimally solved and require problem-specific heuristics or metaheuristics to determine good quality feasible solutions in a reasonable time. We propose two metaheuristic solutions, a Genetic Algorithm (GA) and one based on Simulated Annealing (SA), both creating static schedule tables for tasks by simulating Earliest Deadline First (EDF) dispatching with different task deadlines and offsets. Furthermore, we use a List Scheduling-based heuristic to create the GCLs in platforms featuring a TSN backbone. We evaluate the proposed solution with real-world and synthetic test cases scaled to fit the future requirements of ADAS systems. The results show that our heuristic strategy can find correct solutions that meet the complex timing and dependency constraints at a higher rate than the related work approaches, i.e., the jitter constraints are satisfied in over 6 times more cases, and the task chain constraints are satisfied in 41% more cases on average. Our method scales well with the growing trend of ADAS platforms.

Irrelevant matches in round-robin tournaments

We consider tournaments played by a set of players in order to establish a ranking among them. We introduce the notion of irrelevant match, as a match that does not influence the ultimate ranking of the involved parties. After discussing the basic properties of this notion, we seek out tournaments that have no irrelevant matches, focusing on the class of tournaments where each player challenges each other exactly once. We prove that tournaments with a static schedule and at least five players always include irrelevant matches. Conversely, dynamic schedules for an arbitrary number of players can be devised that avoid irrelevant matches, at least for one of the players involved in each match. Finally, we prove by computational means that there exist tournaments where all matches are relevant to both players, at least up to eight players.

Improving Synchronous Dataflow Analysis Supported by Petri Net Mappings

Whereas most of the work that analyses Synchronous Dataflow (SDF) stays in the dataflow framework, this work pushes its analysis into another framework level, thereby addressing issues that are not well addressed or are even unexplored in SDF. In this manner, the paper proposes a model-driven engineering (MDE) method, combining Synchronous Dataflow (SDF) and Petri nets, to highlight and reinforce their interoperability in digital signal processing applications, cyber-physical systems, or industrial applications. Improvements regarding the settlement and exploitation of the initial conditions associated with SDF are demonstrated; this issue is crucial for every cyber-physical system, since a system’s initial conditions are crucial to ensuring the system’s liveness. The improvements outlined in this work exploit an innovating mapping in the Place/Transition (P/T) Petri net domain that is intended to reduce and predict the total amount of initial data in SDF channels. The relevance of the firing semantics engaged with the equivalent Petri net model is discussed. This paper proposes a new approach to estimate whether an SDF has a static schedule by performing simulation and property verification of the equivalent-based P/T Petri net system achieved, framed by a Petri net invariant analysis and based on the stubborn set method of Petri nets. In this way, this new approach will allow mitigating the state explosion problem. Finally, a strategy is applied to two case studies to discover all the elementary circuits (static schedules) associated with the generated model’s state-space.

Scheduling of Periodic Tasks with Data Dependency on Multiprocessors

This article studies the scheduling problem of a set of tasks with time or data constraints on a number of identical processors with full connections. We present an algorithm, in which a set of static schedule lists can be obtained, each for a processor, such that each task starts executing after its release time and completes its computation before its deadline, and all the precedence relations between tasks resulting from data dependency are satisfied. The data dependency relations between tasks are represented by Synchronous Dataflow Graphs (SDF) as they can indicate tasks concurrency and enable effective scheduling on multiprocessor platforms. The SDF, however, does not support the time constraints of tasks directly, thus an adaption is applied to conform to the time limits. With this adaption, the periodic tasks of implicit-deadline or constrained-deadline can be scheduled on multiprocessor platform effectively.