Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems

Zhu, Qi; Huang, Chao; Jiao, Ruochen; Lan, Shuyue; Liang, Hengyi; Wang, Yixuan; Wang, Zhilu; Xu, Shichao

doi:10.1145/3394885.3431623

Cited by 20 publications

(6 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[38] acquires a strategic level k planner for merging in dense traffic by reinforcement learning and iterative reasoning. But these learning-based methods are hard and expensive to assure safety [39], [40], [41], [42], [43], [44].…”

Section: B Interaction In Dense Trafficmentioning

confidence: 99%

Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic

Chen¹,

Li²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

When the traffic stream is extremely congested and surrounding vehicles are not cooperative, the mandatory lane changing can be significantly difficult. In this work, we propose an interactive trajectory planner, which will firstly attempt to change lanes as long as safety is ensured. Based on receding horizon planning, the ego vehicle can abort or continue changing lanes according to surrounding vehicles' reactions. We demonstrate the performance of our planner in extensive simulations with eight surrounding vehicles, initial velocity ranging from 0.5 to 5 meters per second, and bumper to bumper gap ranging from 4 to 10 meters. The ego vehicle with our planner can change lanes safely and smoothly. The computation time of the planner at every step is within 10 milliseconds in most cases on a laptop with 1.8GHz Intel Core i7-10610U.

show abstract

Section: B Interaction In Dense Trafficmentioning

confidence: 99%

Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic

Chen¹,

Li²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the adoption of connectivity technology faces significant challenges from the increasing complexity in analyzing system behavior and ensuring its safety, especially with the wider usage of neural network based components in vehicle decision making [42]. During the transition period to the next-generation transportation system, a mixed traffic stream of human-driven and autonomous vehicles [38], and connected and non-connected vehicles need to share the transportation network.…”

Section: Introductionmentioning

confidence: 99%

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Jiao¹,

Zheng²,

Liang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior and quantifying uncertainty for highly interactive scenarios, e.g., lane changing. It is even harder to ensure system safety when neural network based planners are leveraged to further improve efficiency. In this work, we propose a connectivity-enhanced neural network based lane changing planner. By cooperating with surrounding connected vehicles in dynamic environment, our proposed planner will adapt its planned trajectory according to the analysis of a safe evasion trajectory. We demonstrate the strength of our planner design in improving efficiency and ensuring safety in various mixed traffic scenarios with extensive simulations. We also analyze the system robustness when the communication or coordination is not perfect.

show abstract

“…Several highprofile fatal traffic accidents involving AVs have been caused by failures of DNNs used for environment perception, e.g., in the 2016 accident that killed a Tesla driver, the Tesla vehicle's brake was not applied since the DNN-based object recognition algorithm misclassified the side of a large white truck as the sky. Despite the widespread use of DNNs in autonomous systems, there is a lack of rigorous and practical Verification and Validation (V&V) techniques for them [4].…”

Section: Introductionmentioning

confidence: 99%

Out-of-Distribution Detection for Deep Neural Networks With Isolation Forest and Local Outlier Factor

Luan

Gu²,

Freidovich

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Deep Neural Networks (DNNs) are extensively deployed in today's safety-critical autonomous systems thanks to their high performance. However, they are known to make mistakes unpredictably, e.g., a DNN may misclassify an object if it is used for perception, or issue unsafe control commands if it is used for planning and control. One common cause for such unpredictable mistakes is Out-of-Distribution (OOD) inputs, i.e., test inputs that fall outside of the distribution of the training dataset. In this paper, we present a framework for OOD detection based on outlier detection in the hidden layers of a DNN by applying Isolation Forest (IF) and Local Outlier Factor (LOF) techniques. Extensive experimental evaluation indicates that LOF is a promising method in terms of both the Machine Learning metrics of precision, recall, F1 score and accuracy, and computational efficiency during testing.

show abstract

Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems

Cited by 20 publications

References 68 publications

Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic

Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Out-of-Distribution Detection for Deep Neural Networks With Isolation Forest and Local Outlier Factor

Contact Info

Product

Resources

About