LiDAR Spoofing Triggers Failures in Autonomous Vehicle Longitudinal Safety Controllers, Study Finds

Global: LiDAR Spoofing Triggers Failures in Autonomous Vehicle Longitudinal Safety Controllers, Study Finds

Researchers have demonstrated that object‑based LiDAR spoofing attacks can compromise the longitudinal safety controllers of autonomous vehicles during highway driving. The findings were posted to the arXiv preprint server in December 2025 and focus on how false object detections affect adaptive cruise control and automatic emergency braking functions.

Simulation Framework and Experimental Setup

A high‑fidelity simulation environment was employed, integrating LiDAR perception models, object‑tracking algorithms, and closed‑loop vehicle control. The framework allowed the team to inject adversarial objects that produced persistent perception errors without altering the vehicle’s control software.

Attack Scenarios Examined

The study evaluated two realistic highway situations: a cut‑in maneuver where a spoofed vehicle appears ahead of the ego car, and a car‑following scenario in which the adversarial object remains in the sensor’s field of view. Both cases introduced false or displaced detections that persisted for short durations.

Observed Safety Impacts

Results indicate that even brief LiDAR‑induced hallucinations can cause unsafe braking events, delayed reactions to genuine hazards, and unstable controller behavior. In cut‑in tests, the frequency of unsafe deceleration and time‑to‑collision violations rose markedly compared with benign baseline runs.

Temporal Consistency Versus Spatial Accuracy

Analysis revealed that the temporal consistency of spoofed objects—how long the false detection persists—has a stronger influence on controller failures than the magnitude of spatial displacement alone. Persistent but modest errors proved more disruptive than brief, large‑magnitude offsets.

Implications for Autonomous Driving Safety

The findings highlight a gap between perception robustness and the safety guarantees expected at the control level. Designers of autonomous systems may need to incorporate attack‑aware safety mechanisms, such as verification layers that assess the plausibility of perceived objects over time.

Future Research Directions

Authors suggest extending the analysis to real‑world hardware experiments and exploring mitigation strategies that combine sensor fusion with temporal anomaly detection. Such work could inform standards for resilient LiDAR‑dependent autonomous driving platforms.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.