Anomaly Detection for Advanced Driver Assistance Systems Using Online Feature Selection

Context: As we move towards higher levels of automation in autonomous driving, we see an increase in functionality that either assists or takes over in both normal and emergency scenarios. These new functionalities can be intentionally switched off by the user, but can also be deactivated unintentionally by (i) accident, (ii) a software malfunction, (iii) a hardware defect, or (iv) an intrusion. Aim: In addition to already applied methods at design time, we aim to recognise mistimed and/or unintended deactivation of vehicle functions, in particular, driver assistance functions (ADAS), at run-time. Upon recognition of the occurrence, we propose to inform the user and the original equipment manufacturer (OEM) in order to improve both the future and the current system behaviour, to support development processes, and to support already conducted safety measures. Method: Based on a feature subset, selected by streaming feature selection, we learn the nominal behaviour of the driver in the interaction with ADAS functions in order to find deviations. The approach considers the technical challenges of automotive E/E architectures and is optimised to reduce communication and computational complexity. We evaluate this approach with recorded real car data from customers participating in a field study. Results: Based on eight datasets, we traced a total of 17 state-of-the-art ADAS functions per participant, yielding to a total of 136 runs. We observed that during 24 among them, the user deactivated the functions at least once for more than a few seconds. For 13 of these 24 runs, we were able to detect and flag possible non-nominal behaviour. Conclusion: As at least one participant configured a convincingly large number of ADAS functions, we need a dynamic system to monitor the configuration of these functions actively. Our approach was capable of detecting potential non-nominal behaviour in up to 52% (13/24) out of these reconfigurations. This result is promising and will receive further attention in future work.

I. Introduction

Today, E/E (electric/electronic) architectures are best characterised as historically grown, mostly federated, partly integrated architectures with often pragmatic, cost-efficient, and ad-hoc solutions. Current trends in the automotive industry are introducing new, increasingly complex software functions into vehicles [1]. The ever-growing availability of computing resources, memory, and newest technologies allows for new levels of automated driving (i. e., levels 3 to 5 according to SAE J3016 [2]) and intelligent systems. Table I: Anomaly model

Possible reasons		Actions
		Vehicle	Driver	OEM
Vehicle	SW/HW fault	depend. pattern, CC message safe state analyse fault
Driver	intentionally, maloperation	CC message, safe state — Improve UI
Environment	IT attack	CC message, containment	safe state	analyse security threat

The structure follows the generic concept of endangerment scenarios and mitigation strategies introduced by Gleirscher and Kugele [4].