  Sensors are crucial for perception and autonomous operation in robotic
vehicles (RV). Unfortunately, RV sensors can be compromised by physical attacks
such as sensor tampering or spoofing. In this paper, we present DeLorean, a
unified framework for attack detection, attack diagnosis, and recovering RVs
from sensor deception attacks (SDA). DeLorean can recover RVs even from strong
SDAs in which the adversary targets multiple heterogeneous sensors
simultaneously. We propose a novel attack diagnosis technique that inspects the
attack-induced errors under SDAs, and identifies the targeted sensors using
causal analysis. DeLorean then uses historic state information to selectively
reconstruct physical states for compromised sensors, enabling targeted attack
recovery under single or multi-sensor SDAs. We evaluate DeLorean on four real
and two simulated RVs under SDAs targeting various sensors, and we find that it
successfully recovers RVs from SDAs in 93% of the cases.
