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Jianing Sun

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EAAI Journal 2026 Journal Article

Adaptive eco-cooperative adaptive cruise control for heterogeneous Vehicle platoons using online identification-informed deep reinforcement learning

Jianing Sun
Chenhao Xiong
Chunjie Zhai
Yuyuan Li
Xiongding Liu
Chuqiao Chen
Chenggang Yan
Yahong Chen

With the advancement of autonomous driving technologies, deep reinforcement learning (DRL) has increasingly played a pivotal role in the control of intelligent connected vehicle (ICV) platoons. Traditional DRL-based platoon control methods often overlook the dynamic discrepancies between vehicles (such as weight and powertrain delays), which results in inadequate adaptability of the control systems. To address this issue, this paper introduces an innovative heterogeneous platoon control method that combines the Adaptive Forgetting Factor Least Squares (AFFLS) algorithm with Proximal Policy Optimization (PPO). The proposed approach leverages the AFFLS algorithm to perform real-time identification of vehicle dynamic parameters, enabling the control system to dynamically adjust to various vehicles. This enhances the platoon stability, passenger comfort, communication delay robustness, and fuel efficiency. Simulation results demonstrate the practical advantages of this approach: maintaining the average inter-vehicle distance error within 0. 01 m, reducing the stabilization time to approximately 3 s, and lowering fuel consumption. Compared to four baseline methods, our method exhibits superior adaptability and robustness. This study offers a novel perspective for the collaborative control of heterogeneous platoons in real-world applications, achieving more efficient and safer vehicle control in dynamic traffic environments.

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AAAI Conference 2020 Conference Paper

Memory Augmented Graph Neural Networks for Sequential Recommendation

Chen Ma
Liheng Ma
Yingxue Zhang
Jianing Sun
Xue Liu
Mark Coates

The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difﬁculty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Speciﬁcally, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on ﬁve real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.

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