ترغب بنشر مسار تعليمي؟ اضغط هنا

Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles

97   0   0.0 ( 0 )
 نشر من قبل Geoffrey Litt
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Widespread adoption of autonomous cars will require greater confidence in their safety than is currently possible. Certified control is a new safety architecture whose goal is two-fold: to achieve a very high level of safety, and to provide a framework for justifiable confidence in that safety. The key idea is a runtime monitor that acts, along with sensor hardware and low-level control and actuators, as a small trusted base, ensuring the safety of the system as a whole. Unfortunately, in current systems complex perception makes the verification even of a runtime monitor challenging. Unlike traditional runtime monitoring, therefore, a certified control monitor does not perform perception and analysis itself. Instead, the main controller assembles evidence that the proposed action is safe into a certificate that is then checked independently by the monitor. This exploits the classic gap between the costs of finding and checking. The controller is assigned the task of finding the certificate, and can thus use the most sophisticated algorithms available (including learning-enabled software); the monitor is assigned only the task of checking, and can thus run quickly and be smaller and formally verifiable. This paper explains the key ideas of certified control and illustrates them with a certificate for LiDAR data and its formal verification. It shows how the architecture dramatically reduces the amount of code to be verified, providing an end-to-end safety analysis that would likely not be achievable in a traditional architecture.



قيم البحث

اقرأ أيضاً

Autonomous vehicles face tremendous challenges while interacting with human drivers in different kinds of scenarios. Developing control methods with safety guarantees while performing interactions with uncertainty is an ongoing research goal. In this paper, we present a real-time safe control framework using bi-level optimization with Control Barrier Function (CBF) that enables an autonomous ego vehicle to interact with human-driven cars in ramp merging scenarios with a consistent safety guarantee. In order to explicitly address motion uncertainty, we propose a novel extension of control barrier functions to a probabilistic setting with provable chance-constrained safety and analyze the feasibility of our control design. The formulated bi-level optimization framework entails first choosing the ego vehicles optimal driving style in terms of safety and primary objective, and then minimally modifying a nominal controller in the context of quadratic programming subject to the probabilistic safety constraints. This allows for adaptation to different driving strategies with a formally provable feasibility guarantee for the ego vehicles safe controller. Experimental results are provided to demonstrate the effectiveness of our proposed approach.
We study a novel principle for safe and efficient collision avoidance that adopts a mathematically elegant and general framework abstracting as much as possible from the controlled vehicles dynamics and of its environment. Vehicle dynamics is charact erized by pre-computed functions for accelerating and braking to a given speed. Environment is modeled by a function of time giving the free distance ahead of the controlled vehicle under the assumption that the obstacles are either fixed or are moving in the same direction. The main result is a control policy enforcing the vehicles speed so as to avoid collision and efficiently use the free distance ahead, provided some initial safety condition holds. The studied principle is applied to the design of two discrete controllers, one synchronous and another asynchronous. We show that both controllers are safe by construction. Furthermore, we show that their efficiency strictly increases for decreasing granularity of discretization. We present implementations of the two controllers, their experimental evaluation in the Carla autonomous driving simulator and investigate various performance issues.
172 - Bo Yang , Yiwen Lu , Xu Yang 2021
Drift control is significant to the safety of autonomous vehicles when there is a sudden loss of traction due to external conditions such as rain or snow. It is a challenging control problem due to the presence of significant sideslip and nearly full saturation of the tires. In this paper, we focus on the control of drift maneuvers following circular paths with either fixed or moving centers, subject to change in the tire-ground interaction, which are common training tasks for drift enthusiasts and can therefore be used as benchmarks of the performance of drift control. In order to achieve the above tasks, we propose a novel hierarchical control architecture which decouples the curvature and center control of the trajectory. In particular, an outer loop stabilizes the center by tuning the target curvature, and an inner loop tracks the curvature using a feedforward/feedback controller enhanced by an $mathcal{L}_1$ adaptive component. The hierarchical architecture is flexible because the inner loop is task-agnostic and adaptive to changes in tire-road interaction, which allows the outer loop to be designed independent of low-level dynamics, opening up the possibility of incorporating sophisticated planning algorithms. We implement our control strategy on a simulation platform as well as on a 1/10 scale Radio-Control~(RC) car, and both the simulation and experiment results illustrate the effectiveness of our strategy in achieving the above described set of drift maneuvering tasks.
Autonomous vehicles bring the promise of enhancing the consumer experience in terms of comfort and convenience and, in particular, the safety of the autonomous vehicle. Safety functions in autonomous vehicles such as Automatic Emergency Braking and L ane Centering Assist rely on computation, information sharing, and the timely actuation of the safety functions. One opportunity to achieve robust autonomous vehicle safety is by enhancing the robustness of in-vehicle networking architectures that support built-in resiliency mechanisms. Software Defined Networking (SDN) is an advanced networking paradigm that allows fine-grained manipulation of routing tables and routing engines and the implementation of complex features such as failover, which is a mechanism of protecting in-vehicle networks from failure, and in which a standby link automatically takes over once the main link fails. In this paper, we leverage SDN network programmability features to enable resiliency in the autonomous vehicle realm. We demonstrate that a Software Defined In-Vehicle Networking (SDIVN) does not add overhead compared to Legacy In-Vehicle Networks (LIVNs) under non-failure conditions and we highlight its superiority in the case of a link failure and its timely delivery of messages. We verify the proposed architectures benefits using a simulation environment that we have developed and we validate our design choices through testing and simulations
112 - Fei Ye , Shen Zhang , Pin Wang 2021
In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. Many existing contributions can be attributed to the pipeline approach, wh ich consists of many hand-crafted modules, each with a functionality selected for the ease of human interpretation. However, this approach does not automatically guarantee maximal performance due to the lack of a system-level optimization. Therefore, this paper also presents a growing trend of work that falls into the end-to-end approach, which typically offers better performance and smaller system scales. However, their performance also suffers from the lack of expert data and generalization issues. Finally, the remaining challenges applying deep RL algorithms on autonomous driving are summarized, and future research directions are also presented to tackle these challenges.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا