A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed

Hybrid systems in automotive electronics design

Automotive electronic design is certainly one of the most attractive and promising application domains for hybrid system techniques. Some successful hybrid system applications to automotive model development and control algorithm design have already been reported in the literature. However, despite the significant advances achieved in the past few years, hybrid methods are in general still not mature enough for their effective introduction in the automotive industry design processes at large. In this paper, we take a broad view of the development process for embedded control systems in the automotive industry with the purpose of identifying challenges and additional opportunities for hybrid systems. We identify critical steps in the design flow and extract a number of open problems where hybrid system technology might play an important role

A Satisfiability Modulo Theory Approach to Secure State Reconstruction in Differentially Flat Systems Under Sensor Attacks

We address the problem of estimating the state of a differentially flat system from measurements that may be corrupted by an adversarial attack. In cyber-physical systems, malicious attacks can directly compromise the system's sensors or manipulate the communication between sensors and controllers. We consider attacks that only corrupt a subset of sensor measurements. We show that the possibility of reconstructing the state under such attacks is characterized by a suitable generalization of the notion of s-sparse observability, previously introduced by some of the authors in the linear case. We also extend our previous work on the use of Satisfiability Modulo Theory solvers to estimate the state under sensor attacks to the context of differentially flat systems. The effectiveness of our approach is illustrated on the problem of controlling a quadrotor under sensor attacks.Comment: arXiv admin note: text overlap with arXiv:1412.432

Digital Sensitivity: Predicting signal interaction using functional analysis

Abstract Maintaining signal integrity in digital systems is becoming increasingly dicult due to the rising number of analog effects seen in deep sub-micron design. One such eect, the signal crosstalk problem, is now a serious design concern. Signals which couple electrically may not aect system behavior because of timing or function in the digital domain. If we can isolate observable coupling eects then we can constrain layout synthesis to eliminate the

Reachability computation for hybrid systems with Ariadne

Ariadne is an in-progress open environment to design algorithms for computing with hybrid automata, that relies on a rigorous computable analysis theory to represent geometric objects, in order to achieve provable approximation bounds along the computations. In this paper we discuss the problem of reachability analysis of hybrid automata to decide safety properties. We describe in details the algorithm used in Ariadne to compute over-approximations of reachable sets. Then we show how it works on a simple example. Finally, we discuss the lower-approximation approach to the reachability problem and how to extend Ariadne to support it

An Efficient Wire Routing and Wire Sizing Algorithm for Weight Minimization of Automotive Systems

As the complexities of automotive systems increase, designing a system is a difficult task that cannot be done manually. In this paper, we propose an algorithm for weight minimization of wires used for connecting electronic devices in a system. The wire routing problem is formulated as a Steiner tree problem with capacity constraints, and the location of a Steiner vertex is selected for adding a splice connecting more than two wires. Besides wire routing, wire sizing is also done to satisfy resistance constraints and minimize the total wiring weight. Experimental results show the effectiveness and efficiency of our algorithm. Copyright 2014 ACM

A Contract-Based Methodology for Aircraft Electric Power System Design

In an aircraft electric power system, one or more supervisory control units actuate a set of electromechanical switches to dynamically distribute power from generators to loads, while satisfying safety, reliability, and real-time performance requirements. To reduce expensive redesign steps, this control problem is generally addressed by minor incremental changes on top of consolidated solutions. A more systematic approach is hindered by a lack of rigorous design methodologies that allow estimating the impact of earlier design decisions on the final implementation. To achieve an optimal implementation that satisfies a set of requirements, we propose a platform-based methodology for electric power system design, which enables independent implementation of system topology (i.e., interconnection among elements) and control protocol by using a compositional approach. In our flow, design space exploration is carried out as a sequence of refinement steps from the initial specification toward a final implementation by mapping higher level behavioral and performance models into a set of either existing or virtual library components at the lower level of abstraction. Specifications are first expressed using the formalisms of linear temporal logic, signal temporal logic, and arithmetic constraints on Boolean variables. To reason about different requirements, we use specialized analysis and synthesis frameworks and formulate assume guarantee contracts at the articulation points in the design flow. We show the effectiveness of our approach on a proof-of-concept electric power system design

3D Environment Modeling for Falsification and Beyond with Scenic 3.0

We present a major new version of Scenic, a probabilistic programming language for writing formal models of the environments of cyber-physical systems. Scenic has been successfully used for the design and analysis of CPS in a variety of domains, but earlier versions are limited to environments which are essentially two-dimensional. In this paper, we extend Scenic with native support for 3D geometry, introducing new syntax which provides expressive ways to describe 3D configurations while preserving the simplicity and readability of the language. We replace Scenic's simplistic representation of objects as boxes with precise modeling of complex shapes, including a ray tracing-based visibility system that accounts for object occlusion. We also extend the language to support arbitrary temporal requirements expressed in LTL, and build an extensible Scenic parser generated from a formal grammar of the language. Finally, we illustrate the new application domains these features enable with case studies that would have been impossible to accurately model in Scenic 2.Comment: 13 pages, 6 figures. Full version of a CAV 2023 tool paper, to appear in the Springer Lecture Notes in Computer Science serie