This layer contains the information of the background adversarial vehicles whose behaviors are not directly guided by LinguaSim. These vehicles are automatically generated and placed around the ego vehicle and the guided adversarial vehicles by LLM agent Chaos Maker, and roam aimlessly on the given map. The background vehicles significantly increase the uncertainty and complexity of the generated scenarios. B. Adversarial Behavior Generation Compared to other state-of-the-art methods for generating 3D realistic scenarios from natural language descriptions, LinguaSim achieves a higher level of realism, flexibility, and interactivity due to the innovative structure of its Action Generator agent. The detailed workflow of this component will be elaborated further in this section, with a simplified operational logic of the Action Generator illustrated in Figure 1. Figure 1: The basic workflow of module Action Generator To establish a solid foundation for the Action Generator, a retrieval database was constructed to store various behaviors available for the guided adversarial vehicles. Each behavior in the database is referred to as an Atomic Behavior, serving as a fundamental component in the subsequent process. As illustrated in Figure 1, each Atomic Behavior comprises three essential parts: 1) Agent Selection: An autonomous driving agent is selected to guide the adversarial vehicle to which Figure 1: An example of the Behavior T opology W eb generated by the Action Generator the Atomic Behavior is applied. LinguaSim includes various predefined agents, such as the basic CARLA built-in agent that follows a given route, an auto cruise control (ACC) agent that follows the vehicle in front, or the PlanT agent, an imitation-learning-based planning algorithm developed by Renz, Chitta et al. [10]. These agents serve different purposes; for example, the F ollow V ehicle behavior uses the ACC agent, while the PlanT agent is often used for less aggressive behaviors to mimic cautious drivers.

Scenario Description Languages (SDLs) provide structured, interpretable embeddings that represent traffic scenarios encountered by autonomous vehicles (AVs), supporting key tasks such as scenario similarity searches and edge case detection for safety analysis. This paper introduces the Trajectory-to-Action Pipeline (TAP), a scalable and automated method for extracting SDL labels from large trajectory datasets. TAP applies a rules-based cross-entropy optimization approach to learn parameters directly from data, enhancing generalization across diverse driving contexts. Using the Waymo Open Motion Dataset (WOMD), TAP achieves 30% greater precision than Average Displacement Error (ADE) and 24% over Dynamic Time Warping (DTW) in identifying behaviorally similar trajectories. Additionally, TAP enables automated detection of unique driving behaviors, streamlining safety evaluation processes for AV testing. This work provides a foundation for scalable scenario-based AV behavior analysis, with potential extensions for integrating multi-agent contexts.

With the rapid development of urban transportation and the continuous advancement in autonomous vehicles, the demand for safely and efficiently testing autonomous driving and traffic optimization algorithms arises, which needs accurate modeling of large-scale urban traffic scenarios. Existing traffic simulation systems encounter two significant limitations. Firstly, they often rely on open-source datasets or manually crafted maps, constraining the scale of simulations. Secondly, vehicle models within these systems tend to be either oversimplified or lack controllability, compromising the authenticity and diversity of the simulations. In this paper, we propose LCSim, a large-scale controllable traffic simulator. LCSim provides map tools for constructing unified high-definition map (HD map) descriptions from open-source datasets including Waymo and Argoverse or publicly available data sources like OpenStreetMap to scale up the simulation scenarios. Also, we integrate diffusion-based traffic simulation into the simulator for realistic and controllable microscopic traffic flow modeling. By leveraging these features, LCSim provides realistic and diverse virtual traffic environments.

In autonomous driving, predicting the behavior (turning left, stopping, etc.) of target vehicles is crucial for the self-driving vehicle to make safe decisions and avoid accidents. Existing deep learning-based methods have shown excellent and accurate performance, but the black-box nature makes it untrustworthy to apply them in practical use. In this work, we explore the interpretability of behavior prediction of target vehicles by an Episodic Memory implanted Neural Decision Tree (abbrev. eMem-NDT). The structure of eMem-NDT is constructed by hierarchically clustering the text embedding of vehicle behavior descriptions. eMem-NDT is a neural-backed part of a pre-trained deep learning model by changing the soft-max layer of the deep model to eMem-NDT, for grouping and aligning the memory prototypes of the historical vehicle behavior features in training data on a neural decision tree. Each leaf node of eMem-NDT is modeled by a neural network for aligning the behavior memory prototypes. By eMem-NDT, we infer each instance in behavior prediction of vehicles by bottom-up Memory Prototype Matching (MPM) (searching the appropriate leaf node and the links to the root node) and top-down Leaf Link Aggregation (LLA) (obtaining the probability of future behaviors of vehicles for certain instances). We validate eMem-NDT on BLVD and LOKI datasets, and the results show that our model can obtain a superior performance to other methods with clear explainability. The code is available at https://github.com/JWFangit/eMem-NDT.

As concerns over security risks for connected vehicles continue to build, automotive cybersecurity company SafeRide Technologies believes unsupervised machine learning will help keep threat actors out of the driver's seats. Earlier this month, SafeRide launched its vXRay technology for connected vehicles' security operations center (SOC), which uses unsupervised machine learning technology to provide behavioral profiling and anomaly detection to improve connected vehicle security. Gil Reiter, vice president of product management and marketing at SafeRide, based in Tel Aviv, Israel, said vXRay is available for OEMs and fleet managers to integrate in their vehicles' SOC. "The vXRay technology establishes the normal behavior of the vehicle without any dependencies or without any knowledge of the specific electronic control unit properties," Reiter said. "Once the behavioral baseline of the vehicle is established, the technology can accurately detect and then flag any abnormal behavior of the vehicle system and report the abnormal behavior to the connected vehicle's SOC for further analysis."