Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles (original) (raw)
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Statistical Model Checking Applied on Perception and Decision-making Systems for Autonomous Driving
2018
Automotive systems must undergo a strict process of validation before their release on commercial vehicles. The currently-used methods are not adapted to latest autonomous systems, which increasingly use probabilistic approaches. Furthermore , real life validation, when even possible, often imply costs which can be obstructive. New methods for validation and testing are necessary. In this paper, we propose a generic method to evaluate complex automotive-oriented systems for automation (perception, decision-making, etc.). The method is based on Statistical Model Checking (SMC), using specifically defined Key Performance Indicators (KPIs), as temporal properties depending on a set of identified metrics. By feeding the values of these metrics during a large number of simulations, and the properties representing the KPIs to our statistical model checker, we evaluate the probability to meet the KPIs. We applied this method to two different subsystems of an autonomous vehicles: a percepti...
2019
Automotive systems must undergo a strict process of validation before their release on commercial vehicles. With the increased use of probabilistic approaches in autonomous systems, standard validation methods are not applicable to this end. Furthermore, real life validation, when even possible, implies costs which can be obstructive. New methods for validation and testing are thus necessary. In this paper, we propose a generic method to evaluate complex probabilistic frameworks for autonomous driving. The method is based on Statistical Model Checking (SMC), using specifically defined Key Performance Indicators (KPIs), as temporal properties depending on a set of identified metrics. By studying the behavior of these metrics during a large number of simulations via our statistical model checker, we finally evaluate the probability for the system to meet the KPIs. We show how this method can be applied to two different subsystems of an autonomous vehicle: a perception system and a decisionmaking approach. An overview of these two systems is given to understand related validation challenges. Extensive validation results are then provided for the decision-making case.
Verification of Decision Making Software in an Autonomous Vehicle: An Industrial Case Study
2019
Correctness of autonomous driving systems is crucial as incorrect behaviour may have catastrophic consequences. Many different hardware and software components (e.g. sensing, decision making, actuation, and control) interact to solve the autonomous driving task, leading to a level of complexity that brings new challenges for the formal verification community. Though formal verification has been used to prove correctness of software, there are significant challenges in transferring such techniques to an agile software development process and to ensure widespread industrial adoption. In the light of these challenges, the identification of appropriate formalisms, and consequently the right verification tools, has significant impact on addressing them. In this paper, we evaluate the application of different formal techniques from supervisory control theory, model checking, and deductive verification to verify existing decision and control software (in development) for an autonomous vehi...
Model-based Verification and Validation of an Autonomous Vehicle System
arXiv (Cornell University), 2018
The software development for Cyber-Physical Systems (CPS), e.g., autonomous vehicles, requires both functional and non-functional quality assurance to guarantee that the CPS operates safely and effectively. EAST-ADL is a domain specific architectural language dedicated to safety-critical automotive embedded system design. We have previously modified EAST-ADL to include energy constraints and transformed energy-aware real-time (ERT) behaviors modeled in EAST-ADL/STATEFLOW into UPPAAL models amenable to formal verification. Previous work is extended in this paper by including support for SIMULINK and an integration of Simulink/Stateflow within a same tool-chain. Simulink/Stateflow models are transformed, based on extended ERT constraints in EAST-ADL, into verifiable UPPAAL models with stochastic semantics and integrate the translation with formal statistical analysis techniques: Probabilistic extension of EAST-ADL constraints is defined as a semantics denotation. A set of mapping rules is proposed to facilitate the guarantee of translation. Formal analysis on both functional-and non-functional properties is performed using SIMULINK DESIGN VERIFIER/UPPAAL-SMC. The analysis techniques are validated and demonstrated on the autonomous traffic sign recognition vehicle case study.
2017
Autonomous vehicles’ behavioural analysis represents a major challenge in the automotive world. In order to ensure safety and fluidity of driving, various methods are available, in particular, simulation and formal verification. The analysis, however, has to cope with very complex environments depending on many parameters evolving in real time. In this context, none of the aforementioned approaches is fully satisfactory, which lead us to propose a combined methodology in order to point out suspicious behaviours more efficiently. We illustrate this approach by studying a non deterministic scenario involving a vehicle, which has to react to some perilous situation.
Towards a Two-Layer Framework for Verifying Autonomous Vehicles
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Autonomous vehicles rely heavily on intelligent algorithms for path planning and collision avoidance, and their functionality and dependability can be ensured through formal verification. To facilitate the verification, it is beneficial to decouple the static high-level planning from the dynamic functions like collision avoidance. In this paper, we propose a conceptual two-layer framework for verifying autonomous vehicles, which consists of a static layer and a dynamic layer. We focus concretely on modeling and verifying the dynamic layer using hybrid automata and uppaal smc, where a continuous movement of the vehicle as well as collision avoidance via a dipole flow field algorithm are considered. In our framework, decoupling is achieved by separating the verification of the vehicle's autonomous path planning from that of the vehicle autonomous operation in its continuous dynamic environment. To simplify the modeling process, we propose a pattern-based design method, where patterns are expressed as hybrid automata. We demonstrate the applicability of the dynamic layer of our framework on an industrial prototype of an autonomous wheel loader.
2020 IEEE Intelligent Vehicles Symposium (IV), 2020
A crucial aspect that automotive systems need to face before being used in everyday life is the validation of their components. To this end, standard exhaustive methods are inappropriate to validate the probabilistic algorithms widely used in this field and new solutions need to be adopted. In this paper, we present an approach based on Statistical Model Checking (SMC) to validate the collision risk assessment generated by a probabilistic perception system. SMC represents an intermediate between test and exhaustive verification by relying on statistics and evaluates the probability of meeting appropriate Key Performance Indicators (KPIs) based on a large number of simulations. As a case study, a state-of-the-art algorithm is adopted to obtain the collision risk estimations. This algorithm provides an environment representation through Bayesian probabilistic occupancy grids and estimates positions in the near future of every static and dynamic part of the grid. Based on these estimations, time-to-collision probabilities are then associated with the corresponding cells. Using CARLA simulator, a large number of execution traces are then generated, considering both collisions and almost-collisions in realistic urban scenarios. Real experiments complete the analysis and show the reliability of the simulation results.
Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review
IEEE Access, 2021
Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities.
Probabilistic logic Markov decision processes for modeling driving behaviors in self-driving cars
Ibero-American Conference on Artificial Intelligence, 2022
In this paper, we propose to use probabilistic logic to generate action policies to select driving behaviours for autonomous cars. Probabilistic logic combines uncertainty handling of probability models with the expressiveness of first order logic as a knowledge representation. Action policies are obtained from a probabilistic logic description of Markov decision processes and it dictates the optimal action to be performed accordingly to the existence and behaviour of other nearby vehicles. We use a realistic simulator to validate our proposal. In addition to the ego car, the environment includes a road with two one-way lanes and four vehicles. The perceptual capabilities of our autonomous car incorporates sensors for the identification of the lane of the road, the presence and velocity of surrounding vehicles, and collisions of the selfdriving. The navigation capabilities implement lane tracking, obstacle avoidance, and the execution of the four basic actions considered in this work: stopping, braking, keeping distance and passing. We evaluate the performance of our ego car in 32 different driving scenarios. Results show the suitability of the global approach to achieve a safe navigation and the appropriateness of the probabilistic logic description to effectively model driving behaviours of the autonomous car.
Application of formal verification to the lane change module of an autonomous vehicle
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