Vehicle Acceleration Prediction Considering Environmental Influence and Individual Driving Behavior
This work addresses accurate acceleration prediction for intelligent driving control and energy efficiency, though it appears incremental as it combines existing methods (LSTM with attention) with new data features.
The paper tackles vehicle acceleration prediction by jointly modeling environmental influence and individual driving behavior, achieving a 33% accuracy improvement with driver classification and a 10.9% improvement with historical traffic variables compared to baselines.
Accurate vehicle acceleration prediction is critical for intelligent driving control and energy efficiency management, particularly in environments with complex driving behavior dynamics. This paper proposes a general short-term vehicle acceleration prediction framework that jointly models environmental influence and individual driving behavior. The framework adopts a dual input design by incorporating environmental sequences, constructed from historical traffic variables such as percentile-based speed and acceleration statistics of multiple vehicles at specific spatial locations, capture group-level driving behavior influenced by the traffic environment. In parallel, individual driving behavior sequences represent motion characteristics of the target vehicle prior to the prediction point, reflecting personalized driving styles. These two inputs are processed using an LSTM Seq2Seq model enhanced with an attention mechanism, enabling accurate multi-step acceleration prediction. To demonstrate the effectiveness of the proposed method, an empirical study was conducted using high resolution radar video fused trajectory data collected from the exit section of the Guangzhou Baishi Tunnel. Drivers were clustered into three categories conservative, moderate, and aggressive based on key behavioral indicators, and a dedicated prediction model was trained for each group to account for driver heterogeneity.Experimental results show that the proposed method consistently outperforms four baseline models, yielding a 10.9% improvement in accuracy with the inclusion of historical traffic variables and a 33% improvement with driver classification. Although prediction errors increase with forecast distance, incorporating environment- and behavior-aware features significantly enhances model robustness.