Modern methods for autonomous driving perception widely adopt multi-modal fusion to enhance 3D scene understanding. However, existing methods suffer from inferior semantic extraction in image encoders that treat all pixels equally, ignoring contextual differences. The generated multi-modal representations also typically lack comprehensive semantic and spatial geometry information, which is crucial for the 3D panoptic segmentation task. In this paper, we propose a novel Semantic-Geometry Fusion Transformer (SGFormer) that extracts adaptive semantic contexts, aggregates geometric information and captures the semantic-geometry fusion. First, in the Image Branch, we tailor semantic contexts for each pixel with context-guided attention and spatial context alignment to refine semantic details. Second, we transform image and voxel features into point-pixel geometry representations, simultaneously learning semantic category priors as embeddings to better represent scene geometry and semantics. Finally, to aggregate semantic information with related geometry, we design a semantic-geometry fusion that combines the transformer, effectively capturing semantic-geometry relationships into multi-modal panoptic representations. Notably, SGFormer achieves the state-of-the-art (SOTA) results on the nuScenes and SemanticPOSS, as well as yielding competitive performance on the SemanticKITTI. Moreover, SGFormer exhibits superior robustness compared to leading methods, marking an improvement of 2% to 10%.

Due to recent advances in large language models and robotics, social robots will potentially play an important role in people’s daily lives soon, and are expected to improve dynamic multi-party group discussions in social scenarios. In this paper, we developed a system to assist dynamic group discussion with our social robot Haru. Our system is composed of three modules: a Dialogue Assistance module via integrating Haru with large language models which facilitates Haru to be an embodied chatbot; a Balancing and Welcoming Behavior module to improve users’ engagement and welcome new users to join the discussion with verbal behaviors; an Autonomous Eye Gazing module to show politeness during group discussion, e.g., gazing to the talking user or the less-engaging user to encourage her, looking to the new comer when she joins the discussion, gazing via eyeball movement when the current speaking user is close to the previous one. The autonomous eye gazing behavior was first trained via deep reinforcement learning in simulation and transferred to physical Haru in the real world. Results of our user study with 50 subjects show the significant performance of our system in assisting dynamic group discussion.

Effective and robust 3D panoptic segmentation is crucial for scene perception in autonomous driving. Modern methods widely adopt multi-modal fusion based simple feature concatenation to enhance 3D scene understanding, resulting in generated multi-modal representations typically lack comprehensive semantic and geometry information. These methods focused on panoptic prediction in a single step also limit the capability to progressively refine panoptic predictions under varying noise levels, which is essential for enhancing model robustness. To address these limitations, we first utilize BEV space to unify semantic-geometry perceptual representation, allowing for a more effective integration of LiDAR and camera data. Then, we propose PrimePSegter, a progressively combined diffusion 3D panoptic segmentation model that is conditioned on BEV maps to iteratively refine predictions by denoising samples generated from Gaussian distribution. PrimePSegter adopts a conditional encoder-decoder architecture for fine-grained panoptic predictions. Specifically, a multi-modal conditional encoder is equipped with BEV fusion network to integrate semantic and geometric information from LiDAR and camera streams into unified BEV space. Additionally, a diffusion transformer decoder operates on multi-modal BEV features with varying noise levels to guide the training of diffusion model, refining the BEV panoptic representations enriched with semantics and geometry in a progressive way. PrimePSegter achieves state-of-the-art performance on the nuScenes and competitive results on the SemanticKITTI, respectively. Moreover, PrimePSegter demonstrates superior robustness towards various scenarios, outperforming leading methods.

Socially assistive robots are potentially to be integrated with human daily lives in the near future, and expected to be able to improve group dynamics when interacting with groups of people in social settings. In this paper, we developed a system with desktop robot Haru to assist group discussions. The system consists of three modules: a dialogue assistance module which facilitates Haru to speak to users and answer questions in a free way; a dialogue balance module to encourage participation of users in the discussion with verbal behaviors; an autonomous gazing behavior module trained via deep reinforcement learning in simulation and deployed on physical Haru in reality, which can show politeness during group discussion, e.g., gazing to the speaking member, looking to the middle when both members are talking or silent, looking at the least spoken person when encouraging her. Results of user study with 40 subjects show the significant effectiveness of our system in assisting group discussion.

Multi-camera vehicle tracking is a fundamental task for city traffic management to count traffic flow or monitor roads. This paper focuses on multi-camera tracking on the highway, which is more challenging compared with city streets in some problems such as fast-moving vehicles, tiny similar vehicles in appearance, longer tracking distance, and lighting intensity changes in the dark tunnels. In this paper, we propose a practical Appearance-Parsing Spatio-Temporal Trajectory Matching Network (ASTM-Net) based on the global appearance matching of local trajectory for addressing the cross-camera tracking tasks on the highway. Specifically, considering that the environmental disturbance and small vehicles have a similar appearance, we propose a multiple appearance-attribute parsing (MAP) module consisting of a Bi-propagation top-down (Bi-TD) block and appearance re-identification (ARe-ID) block to obtain salient global appearance-attribute features through given a video sequence. To address discrete tracking fragments caused by occlusion, we develop an appearance-joint-tracking (AJT) mechanism to merge the isolated tracklets with target interaction and occlusion handling. We then exploit an appearance-informed spatio-temporal matching (ASTM) module to achieve multi-camera tracklet-totarget assignment, which employs spatio-temporal consistency relation for intra-camera trajectory correction and coarse intercamera tracklet correlation and aggregate appearance matrix of local trajectories for assigning global trajectory ID. Finally, in order to evaluate our proposed ASTM-Net, a new dataset, named HST, collected on the highway is established.We verify the ASTM-Net on the HST and the other three public datasets,i.e., CityFlow, UA-DETRAC, and Synthehicle, whose experimental results demonstrate the effectiveness and robustness of the proposed method.