Navigation algorithms are responsible for decision-making in autonomous vehicles, generating safe trajectories based on post-processed sensor data to prevent collisions. However, failures in sensors or software modules can cause deviations from nominal behavior, potentially leading to unsafe situations. To comply with the ISO 26262 functional safety standard, road vehicles must perform a transition to a safe state when a failure is detected, until the corresponding safety goal is reached. This paper presents a novel control framework for navigation in autonomous vehicles, integrating data-driven reachability analysis and zonotopic predictive control to ensure functional safety (FuSa) under nominal conditions and potential system-level anomalies. The proposed framework explicitly accounts for interactions between vehicles, surrounding traffic participants, and infrastructure by incorporating traffic and infrastructure constraints into the control formulation. The proposed approach uses signal temporal logic specifications to formally define safe states and safety goals, guiding the zonotopic predictive control in executing safe maneuvers when they are required. The controller also enforces constraints on obstacle avoidance and infrastructure conditions, all represented as intervals or zonotopes for a unified formulation. The complete control framework is validated using data extracted from a real-world autonomous Renault Mégane (SAE Level 3), demonstrating its effectiveness in executing safe recovery maneuvers while ensuring compliance with the functional safety standard in real-world scenarios.

For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns arise from disclosing sensitive measurements to a cloud estimator. To solve this issue, we propose a differentially private set-based estimation protocol that guarantees true state containment in the estimated set and differential privacy for the sensitive measurements throughout the set-based state estimation process within the central and local differential privacy models. Zonotopes are employed in the proposed differentially private set-based estimator, offering computational advantages in set operations. We consider a plant of a non-linear discrete-time dynamical system with bounded modeling uncertainties, sensors that provide sensitive measurements with bounded measurement uncertainties, and a cloud estimator that predicts the system’s state. The privacy-preserving noise perturbs the centers of measurement zonotopes, thereby concealing the precise position of these zonotopes, i.e., ensuring privacy preservation for the sets containing sensitive measurements. Compared to existing research, our approach achieves less privacy loss and utility loss through the central and local differential privacy models by leveraging a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach.

This paper presents algorithms for performing data-driven reachability analysis under temporal logic side information. In certain scenarios, the data-driven reachable sets of a robot can be prohibitively conservative due to the inherent noise in the robot's historical measurement data. In the same scenarios, we often have side information about the robot's expected motion (e.g., limits on how much a robot can move in a one-time step) that could be useful for further specifying the reachability analysis. In this work, we show that if we can model this side information using a signal temporal logic (STL) fragment, we can constrain the data-driven reachability analysis and safely limit the conservatism of the computed reachable sets. Moreover, we provide formal guarantees that, even after incorporating side information, the computed reachable sets still properly over-approximate the robot's future states. Lastly, we empirically validate the prac-ticality of the over-approximation by computing constrained, data-driven reachable sets for the Small- Vehicles-for-Autonomy (SVEA) hardware platform in two driving scenarios.

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to adjust the ego-vehicle speed appropriately to maintain a proper safety margin. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.

This demo abstract presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable efficient and accurate computation of a target's location while preserving the privacy of the observers.

Aided by advances in sensors and algorithms, systems for localizing and tracking target objects or events have become ubiquitous in recent years. Most of these systems operate on the principle of fusing measurements of distance and/or direction to the target made by a set of spatially distributed observers using sensors that measure signals such as RF, acoustic, or optical. The computation of the target's location is done using multilateration and multiangulation algorithms, typically running at an aggregation node that, in addition to the distance/direction measurements, also needs to know the observers' locations. This presents a privacy risk for an observer that does not trust the aggregation node or other observers and could in turn lead to lack of participation. For example, consider a crowd-sourced sensing system where citizens are required to report security threats, or a smart car, stranded with a malfunctioning GPS, sending out localization requests to neighboring cars - in both cases, observer (i.e., citizens and cars respectively) participation can be increased by keeping their location private. This paper presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable efficient and accurate computation of a target's location without requiring observers to make public their locations or measurements. Moreover, and unlike previously proposed perturbation based techniques, PrOLoc is also resilient to malicious active false data injection attacks. We present two realizations of our approach, provide rigorous theoretical guarantees, and also compare the performance of each against traditional methods. Our experiments on real hardware demonstrate that PrOLoc yields location estimates that are accurate while being at least 500x faster than state-of-art secure function evaluation techniques.

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an inefficient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The first two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error.

Hardware Trojan detection is a very important topic especially as parts of critical systems which are designed and/or manufactured by untrusted third parties. Most of the current research concentrates on detecting Trojans at the testing phase by comparing the suspected circuit to a golden (trusted) one. However, these attempts do not work in the case of third party IPs, which are black boxes with no golden IPs to trust. In this work, we present novel methods for system protection that alleviate the need for a golden chip. Protection against injected Trojan is done using simple blockage method. We show the practicality of the introduced schemes by providing a proof of concept implementation of the proposed methodology on FPGA. We showed that the overhead is low enough in the simple blockage method. The delay overhead is negligible while the power overhead does not exceed 2%.