This paper identifies, analyzes, and addresses case-level forgetting in continual learning for trajectory prediction. We propose the Dynamically Expandable Interactive Trajectory Predictor (DEITP), a novel framework that preserves previously learned knowledge through a dynamic model expansion mechanism. The mechanism regulates expansion timing by assessing model similarity, thereby controlling model growth while preventing catastrophic forgetting. Furthermore, to operate in realistic task-free settings where task identity is unavailable at test time, we introduce a task identification strategy based on a familiarity autoencoder that selects the most appropriate expert for prediction. Extensive experiments on real-world datasets demonstrate that DEITP substantially mitigates forgetting and achieves zero-forgetting performance when task identities are known.

The entire project package is uploaded. Simply unzip it and run the train and test functions separately; the data have already been preprocessed.

Two standard datasets on travel mode choice are used, namely LPMC and Swissmetro (SM). The LPMC dataset consists of single-day travel diary data obtained by the London Travel Demand Survey from 2012 to 2015. The dataset includes 81,096 samples, each of which corresponds to a trip taken by a person in one of the 17,616 households participating in the survey. The dataset contains four travel modes, namely walking, cycling, public transportation (PT), and car. Moreover, the SM dataset comprises passenger survey data gathered in Switzerland. It includes samples from 1291 passengers across nine travel scenarios designed with three kinds of modes, i.e., train, SM, and car. In the materials, we make separate divisions based on the datasets. Among them, the "data" file contains the original data, the processed data, and the data processing code. The configuration details are described in the "conf" folder. The analysis code is included in the "utilities" file. Then, the codes for the centralized, federated learning framework and the federated meta-learning framework are also attached.

This paper proposes AdvGLOW, a framework generating covert adversarial attacks against autonomous driving perception systems. It uses a Glow-based reversible neural network for bi-directional image-latent space transformation, crafting imperceptible perturbations. Coupling layers and actnorm ensure invertible, effective transformations. The method achieves real-time generation (<50 ms). Experiments on driving datasets and models confirm its stealth and effectiveness, pioneering the use of normalizing flows for physically realizable attacks in this context. Repository includes code and configurations.

Mandatory lane changes pose significant challenges to trajectory planning at intersections, where vehicles are required to change lanes mid-block to reach designated turn lanes before the stop bar. MLCs often generate shockwaves that induce increased vehicle delay and fuel consumption, and the presence of human-driven vehicles in mixed traffic further exacerbates this issue. To address these challenges, this study formulates the joint longitudinal-lateral trajectory planning problem in mixed traffic as a multi-agent reinforcement learning task. We propose SS-MA-PPO, a Simulation-Supervised Multi Agent Proximal Policy Optimization framework, which guides connected and automated vehicles in both acceleration and lane-change decisions. A Simulation-Guided Supervisory Module performs offline trajectory rollouts of human-driver models to assess feasibility and safety, and arbitrates online between rule-based and learned policies. The information of surrounding vehicles is incorporated in the observation to achieve vehicle cooperation, and a transfer learning mechanism is designed to accelerate training.