A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction

Accurate and robust trajectory prediction of neighboring agents is critical for autonomous vehicles traversing in complex scenes. Most methods proposed in recent years are deep learning-based due to their strength in encoding complex interactions. However, unplausible predictions are often generated since they rely heavily on past observations and cannot effectively capture the transient and contingency interactions from sparse samples. In this paper, we propose a hierarchical hybrid framework of deep learning (DL) and reinforcement learning (RL) for multi-agent trajectory prediction, to cope with the challenge of predicting motions shaped by multi-scale interactions. In the DL stage, the traffic scene is divided into multiple intermediate-scale heterogenous graphs based on which Transformer-style GNNs are adopted to encode heterogenous interactions at intermediate and global levels. In the RL stage, we divide the traffic scene into local sub-scenes utilizing the key future points predicted in the DL stage. To emulate the motion planning procedure so as to produce trajectory predictions, a Transformer-based Proximal Policy Optimization (PPO) incorporated with a vehicle kinematics model is devised to plan motions under the dominant influence of microscopic interactions. A multi-objective reward is designed to balance between agent-centric accuracy and scene-wise compatibility. Experimental results show that our proposal matches the state-of-the-arts on the Argoverse forecasting benchmark. It's also revealed by the visualized results that the hierarchical learning framework captures the multi-scale interactions and improves the feasibility and compliance of the predicted trajectories

Mechanical thrombectomy with combined stent retriever and contact aspiration versus stent retriever alone for acute large vessel occlusion: data from ANGEL-ACT registry

Background and purpose An analysis of the ASTER 2 trial revealed similar final recanalisation levels and clinical outcomes in acute large vessel occlusion (LVO) stroke between stent retrieval (SR) alone as a first-line mechanical thrombectomy (MT) technique (SR alone first-line) and concomitant use of contact aspiration (CA) plus SR as a first-line MT technique (SR+CA first-line). The purpose of the present study was to compare the safety and efficacy of SR+CA first-line with those of SR alone first-line for patients with LVO in China.Methods We conducted the present study by using the data from the ANGEL-ACT registry. We divided the selected patients into SR+CA first-line and SR alone first-line groups. We performed logistic regression and generalised linear models with adjustments to compare the angiographic and clinical outcomes, including successful/complete recanalisation after the first technique alone and all procedures, first-pass successful/complete recanalisation, number of passes, 90-day modified Rankin Scale, procedure duration, rescue treatment and intracranial haemorrhage within 24 hours.Results Of the 1233 enrolled patients, 1069 (86.7%) received SR alone first-line, and 164 (13.3%) received SR+CA first-line. SR+CA first-line was associated with more thrombectomy passes (3 (2–4) vs 2 (1–2); β=1.77, 95% CI=1.55 to 1.99, p<0.001), and longer procedure duration (86 (60–129) min vs 80 (50–122) min; β=10.76, 95% CI=1.08 to 20.43, p=0.029) than SR alone first-line group. Other outcomes were comparable (all p>0.05) between the two groups.Conclusions Patients undergoing SR+CA first-line had more thrombectomy passes and longer procedure duration than patients undergoing SR alone first-line. Additionally, we suggested that SR+CA first-line was not superior to SR alone first-line in final recanalisation level, first-pass recanalisation level and 90-day clinical outcomes in the Chinese population