Formal representations of traffic scenarios can be used to generate test cases for the safety verification of autonomous driving. However, most existing methods are limited to highway or highly simplified intersection scenarios due to the intricacy and diversity of traffic scenarios. In response, we propose Traffic Scenario Logic (TSL), which is a spatial-temporal logic designed for modeling and reasoning of urban pedestrian-free traffic scenarios. TSL provides a formal representation of the urban road network that can be derived from OpenDRIVE, i.e., the de facto industry standard of high-definition maps for autonomous driving, enabling the representation of a broad range of traffic scenarios without discretization approximations. We implemented the reasoning of TSL using Telingo, i.e., a solver for temporal programs based on Answer Set Programming, and tested it on different urban road layouts. Demonstrations show the effectiveness of TSL in test scenario generation and its potential value in areas like decision-making and control verification of autonomous driving. The code for TSL reasoning has been open-sourced.

This paper addresses the challenge of generating safety-critical scenarios with multiple adversarial vehicles for testing autonomous vehicles. Such scenarios must be plausible and collision-avoidable while resulting in a collision with the vehicle-under-test. However, the tremendous number of scenarios and the low ratio of plausible scenarios makes previous methods squander primary resources on implausible scenarios, degenerating their efficiency. We propose a two-stage framework called the ASP-based Avoidable Collision Scenario Testbench (A²CoST) to overcome this obstacle and improve efficiency. In the former stage, we apply Answer Set Programming (ASP) for generating plausible logical scenarios. In the latter stage, we use a search algorithm to refine logical scenarios into safety-critical concrete scenarios. We also compute collision-free trajectories in these concrete scenarios while the vehicle-under-test fails to avoid the collision. We empirically show the A²CoST significantly decreases the time consumption for simple scenarios while still effectively generating complex critical scenarios. The comparison with real-world traffic data further demonstrates the value of A²CoST in generating plausible scenarios. The source codes of our method and the baselines are opened at https://github.com/Autonomous-Driving-Safety-Project/AACoST.

Rank-reduction approaches assume that seismic data in the frequency-space domain is of low-rank after a specific pre-transformation. The presence of noise or missing traces will increase the rank; therefore, seismic data can be denoised and recovered via rank-reduction techniques. The iterative weighted project onto the convex set (POCS) framework can be used for noise attenuation and data reconstruction simultaneously. Multichannel singular spectrum analysis (MSSA) is a classic 3D seismic data reconstruction algorithm that rearranges the temporal frequency slices of the data with missing traces into a block Hankel matrix, and then uses randomized singular value decomposition (RSVD) to interpolate slices. To further improve the efficiency and precision of 3D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results. Moreover, FCTN decomposes a fourth-order tensor into four factor contractions, which breaks the limitations that traditional tensor decomposition methods such as CANDECOMP/PARAFAC (CP) and Tucker decomposition cannot establish the connections between different factors, and are less effective at characterizing relationships. The newly proposed approach does not require singular value decomposition (SVD), leading to an overall reduction in computational complexity. Synthetic and field examples are used to compare the performance of our method with MSSA, and our numerical results reveal the better performance of the proposed FCTN decomposition method for seismic data with large gaps or a high missing ratio.