Multi-sensor fusion has been widely used by autonomous vehicles (AVs) to integrate the perception results from different sensing modalities including LiDAR, camera and radar. Despite the rapid development of multi-sensor fusion systems in autonomous driving, their vulnerability to malicious attacks have not been well studied. Although some prior works have studied the attacks against the perception systems of AVs, they only consider a single sensing modality or a camera-LiDAR fusion system, which can not attack the sensor fusion system based on LiDAR, camera, and radar. To fill this research gap, in this paper, we present the first study on the vulnerability of multi-sensor fusion systems that employ LiDAR, camera, and radar. Specifically, we propose a novel attack method that can simultaneously attack all three types of sensing modalities using a single type of adversarial object. The adversarial object can be easily fabricated at low cost, and the proposed attack can be easily performed with high stealthiness and flexibility in practice. Extensive experiments based on a real-world AV testbed show that the proposed attack can continuously hide a target vehicle from the perception system of a victim AV using only two small adversarial objects.
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This content will become publicly available on August 14, 2025
A First Physical-World Trajectory Prediction Attack via LiDAR-induced Deceptions in Autonomous Driving
Trajectory prediction forecasts nearby agents’ moves based
on their historical trajectories. Accurate trajectory prediction
(or prediction in short) is crucial for autonomous vehicles
(AVs). Existing attacks compromise the prediction model of
a victim AV by directly manipulating the historical trajectory
of an attacker AV, which has limited real-world applicability.
This paper, for the first time, explores an indirect attack
approach that induces prediction errors via attacks against
the perception module of a victim AV. Although it has been
shown that physically realizable attacks against LiDAR-based
perception are possible by placing a few objects at strategic
locations, it is still an open challenge to find an object location
from the vast search space in order to launch effective attacks
against prediction under varying victim AV velocities.
Through analysis, we observe that a prediction model is
prone to an attack focusing on a single point in the scene. Consequently,
we propose a novel two-stage attack framework to
realize the single-point attack. The first stage of predictionside
attack efficiently identifies, guided by the distribution of
detection results under object-based attacks against perception,
the state perturbations for the prediction model that are
effective and velocity-insensitive. In the second stage of location
matching, we match the feasible object locations with
the found state perturbations. Our evaluation using a public
autonomous driving dataset shows that our attack causes a collision
rate of up to 63% and various hazardous responses of
the victim AV. The effectiveness of our attack is also demonstrated
on a real testbed car 1. To the best of our knowledge,
this study is the first security analysis spanning from LiDARbased
perception to prediction in autonomous driving, leading
to a realistic attack on prediction. To counteract the proposed
attack, potential defenses are discussed.
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- Award ID(s):
- 2120369
- PAR ID:
- 10552796
- Publisher / Repository:
- USENIX Security
- Date Published:
- Journal Name:
- IEEE security privacy
- ISSN:
- 1540-7993
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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