Alexey Vinel

John Pravin Arockiasamy, Andy Comeca, Victoria Yang et al.

On the way toward full autonomy, sharing roads between automated and autonomous vehicles in so-called mixed traffic is unavoidable. Moreover, even if all vehicles on the road were autonomous, pedestrians would still cross streets. We propose social robots as moderators between autonomous vehicles and vulnerable road users. This paper presents a first proof-of-concept integration of a social robot advising pedestrians in crossing scenarios involving a cooperative automated vehicle. We also discuss key enablers required for designing "robot policeman" in a generic use case of cooperative intersection management. Our work provides a vision of the role of social robotics in future Cooperative Intelligent Transport Systems.

Matej Halinkovic, Nina Masarykova, Alexey Vinel et al.

End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities for autonomous driving. Modular pipelines restrict information flow and can amplify upstream errors. Recent query-based, fully differentiable perception-and-prediction (PnP) models mitigate these issues, yet the complementarity of cameras and LiDAR in the query-space has not been sufficiently explored. Models often rely on fusion schemes that introduce heuristic alignment and discrete selection steps which prevent full utilization of available information and can introduce unwanted bias. We propose Li-ViP3D++, a query-based multimodal PnP framework that introduces Query-Gated Deformable Fusion (QGDF) to integrate multi-view RGB and LiDAR in query space. QGDF (i) aggregates image evidence via masked attention across cameras and feature levels, (ii) extracts LiDAR context through fully differentiable BEV sampling with learned per-query offsets, and (iii) applies query-conditioned gating to adaptively weight visual and geometric cues per agent. The resulting architecture jointly optimizes detection, tracking, and multi-hypothesis trajectory forecasting in a single end-to-end model. On nuScenes, Li-ViP3D++ improves end-to-end behavior and detection quality, achieving higher EPA (0.335) and mAP (0.502) while substantially reducing false positives (FP ratio 0.147), and it is faster than the prior Li-ViP3D variant (139.82 ms vs. 145.91 ms). These results indicate that query-space, fully differentiable camera-LiDAR fusion can increase robustness of end-to-end PnP without sacrificing deployability.

Lei Wan, Prabesh Gupta, Andreas Eich et al.

Perception is a core capability of automated vehicles and has been significantly advanced through modern sensor technologies and artificial intelligence. However, perception systems still face challenges in complex real-world scenarios. To improve robustness against various external factors, multi-sensor fusion techniques are essential, combining the strengths of different sensor modalities. With recent developments in Vehicle-to-Everything (V2X communication, sensor fusion can now extend beyond a single vehicle to a cooperative multi-agent system involving Connected Automated Vehicle (CAV) and intelligent infrastructure. This paper presents VALISENS, an innovative multi-sensor system distributed across multiple agents. It integrates onboard and roadside LiDARs, radars, thermal cameras, and RGB cameras to enhance situational awareness and support cooperative automated driving. The thermal camera adds critical redundancy for perceiving Vulnerable Road User (VRU), while fusion with roadside sensors mitigates visual occlusions and extends the perception range beyond the limits of individual vehicles. We introduce the corresponding perception module built on this sensor system, which includes object detection, tracking, motion forecasting, and high-level data fusion. The proposed system demonstrates the potential of cooperative perception in real-world test environments and lays the groundwork for future Cooperative Intelligent Transport Systems (C-ITS) applications.

Lei Wan, Hannan Ejaz Keen, Alexey Vinel

Connected Autonomous Vehicles (CAVs) benefit from Vehicle-to-Everything (V2X) communication, which enables the exchange of sensor data to achieve Collaborative Perception (CP). To reduce cumulative errors in perception modules and mitigate the visual occlusion, this paper introduces a new task, Collaborative Joint Perception and Prediction (Co-P&P), and provides a conceptual framework for its implementation to improve motion prediction of surrounding objects, thereby enhancing vehicle awareness in complex traffic scenarios. The framework consists of two decoupled core modules, Collaborative Scene Completion (CSC) and Joint Perception and Prediction (P&P) module, which simplify practical deployment and enhance scalability. Additionally, we outline the challenges in Co-P&P and discuss future directions for this research area.