Extending Adaptive Cruise Control with Machine Learning Intrusion Detection Systems
Lotfi Ben Othmane, Yasaswini Konapalli, Naga Prudhvi Mareedu

TL;DR
This paper demonstrates that traditional ACC systems are vulnerable to speed injection attacks, and proposes an IDS-augmented control that switches to emergency braking to enhance safety.
Contribution
It introduces ACC-IDS, a simple augmentation of traditional ACC with intrusion detection, to mitigate attack risks and maintain collision avoidance.
Findings
Kalman filter can be bypassed by high-magnitude speed injections
ACC-IDS effectively detects intrusions and triggers emergency braking
The approach preserves ACC's safety guarantees under attack conditions
Abstract
An Adaptive Cruise Control (ACC) system automatically adjusts the host vehicle's speed to maintain a safe following distance from a lead vehicle. In typical implementations, a feedback controller (e.g., a Proportional-Integral-Derivative (PID) controller) computes the host vehicle's acceleration using a target speed and a spacing error, defined as the difference between the measured inter-vehicle distance and a desired safe distance. ACC is often assumed to be resilient to fault-injection attacks because a Kalman filter (KF) can smooth noisy speed measurements. However, we show--through analytical proofs and simulation results--that a KF can tolerate injected speed values only up to a bounded threshold. When injected values exceed this threshold, the filter can be driven off track, causing the ACC controller to make unsafe acceleration decisions and potentially leading to collisions.…
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Taxonomy
TopicsTraffic control and management · Vehicular Ad Hoc Networks (VANETs) · Autonomous Vehicle Technology and Safety
