Manipulating Trajectory Prediction with Backdoors

Autonomous vehicles ought to predict the surrounding agents' trajectories to allow safe maneuvers in uncertain and complex traffic situations. As companies increasingly apply trajectory prediction in the real world, security becomes a relevant concern. In this paper, we focus on backdoors - a security threat acknowledged in other fields but so far overlooked for trajectory prediction. To this end, we describe and investigate four triggers that could affect trajectory prediction. We then show that these triggers (for example, a braking vehicle), when correlated with a desired output (for example, a curve) during training, cause the desired output of a state-of-the-art trajectory prediction model. In other words, the model has good benign performance but is vulnerable to backdoors. This is the case even if the trigger maneuver is performed by a non-casual agent behind the target vehicle. As a side-effect, our analysis reveals interesting limitations within trajectory prediction models. Finally, we evaluate a range of defenses against backdoors. While some, like simple offroad checks, do not enable detection for all triggers, clustering is a promising candidate to support manual inspection to find backdoors.

Social-Transmotion: Promptable Human Trajectory Prediction

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating the space. To address this, we introduce Social-Transmotion, a generic model that exploits the power of transformers to handle diverse and numerous visual cues, capturing the multi-modal nature of human behavior. We translate the idea of a prompt from Natural Language Processing (NLP) to the task of human trajectory prediction, where a prompt can be a sequence of x-y coordinates on the ground, bounding boxes or body poses. This, in turn, augments trajectory data, leading to enhanced human trajectory prediction. Our model exhibits flexibility and adaptability by capturing spatiotemporal interactions between pedestrians based on the available visual cues, whether they are poses, bounding boxes, or a combination thereof. By the masking technique, we ensure our model's effectiveness even when certain visual cues are unavailable, although performance is further boosted with the presence of comprehensive visual data. We delve into the merits of using 2d versus 3d poses, and a limited set of poses. Additionally, we investigate the spatial and temporal attention map to identify which keypoints and frames of poses are vital for optimizing human trajectory prediction. Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY. The code is publicly available: https://github.com/vita-epfl/social-transmotion

Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

Predicting the trajectories of surrounding agents is an essential ability for robots navigating complex real-world environments. Autonomous vehicles (AV) in particular, can generate safe and efficient path plans by predicting the motion of surrounding road users. Future trajectories of agents can be inferred using two tightly linked cues: the locations and past motion of agents, and the static scene structure. The configuration of the agents may uncover which part of the scene is more relevant, while the scene structure can determine the relative influence of agents on each other's motion. To better model the interdependence of the two cues, we propose a multi-head attention-based model that uses a joint representation of the static scene and agent configuration for generating both keys and values for the attention heads. Moreover, to address the multimodality of future agent motion, we propose to use each attention head to generate a distinct future trajectory of the agent. Our model achieves state of the art results on the publicly available nuScenes dataset and generates diverse future trajectories compliant with scene structure and agent configuration. Additionally, the visualization of attention maps adds a layer of interpretability to the trajectories predicted by the model.

Multi-Head Attention with Joint Agent-Map Representation for Trajectory Prediction in Autonomous Driving

For autonomous vehicles to navigate in urban environment, the ability to predict the possible future behaviors of surrounding vehicles is essential to increase their safety level by avoiding dangerous situations in advance. The behavior anticipation task is mainly based on two tightly linked cues; surrounding agents' recent motions and scene information. The configuration of the agents may uncover which part of the scene is important, while scene structure determines the influential existing agents. To better present this correlation, we deploy multi-head attention on a joint agents and map context. Moreover, to account for the uncertainty of the future, we use an efficient multi-modal probabilistic trajectory prediction model that learns to extract different joint context features and generate diverse possible trajectories accordingly in one forward pass. Results on the publicly available nuScenes dataset prove that our model achieves the performance of existing methods and generates diverse possible future trajectories compliant with scene structure. Most importantly, the visualization of attention maps reveals some of the underlying prediction logic of our approach which increases its interpretability and reliability to deploy in the real world.