LDIPRS: A novel longitudinal driving intention prior recognition technique empowered by TENG and deep learning

For autonomous vehicles, real-time and accurate longitudinal driving intention recognition is crucial as it effectively enhances driving safety and improves the driving experience. This study proposes a novel data and model hybrid-driven fine-grained longitudinal driving intention prior recognition system (LDIPRS). Firstly, the system integrates a human-pedal interaction sensor (HPIS) based on triboelectric nanogenerators for fine-grained longitudinal driving maneuver monitoring and the channel attention (CA)-enhanced convolutional neural network (CBRCNet). The HPIS, integrated into the vehicle's acceleration and brake pedals, is capable of monitoring driver foot movement information in the form of electrical signals before the vehicle responds, achieving data level advance. The collected electrical signals are fed into the CBRCNet network, which models and learns the mapping relationship between these signals and fine-grained longitudinal driving intentions, leading to model level advances. The HPIS completes the capture of longitudinal maneuver information 541 ms before the driving simulator starts to respond at the data level. At the model level, CBRCNet can achieve a recognition accuracy of 96.1 % based on partial response data (50 ms after starting response) rather than complete response data of the HPIS. Finally, our proposed LDIPRS realizes the recognition of emergency braking, rapid acceleration, normal braking, and normal acceleration in advance by 732 ms, 1035 ms, 1757 ms, and 2227 ms, respectively. This study introduces self-powered, low-cost, highly sensitive triboelectric sensors into the field of intention recognition, and combines the triboelectric sensors with deep learning algorithms to offer a promising solution to improve the safety of autonomous vehicles and the efficiency of intelligent transportation systems. For autonomous vehicles, real-time and accurate longitudinal driving intention recognition is crucial as it effectively enhances driving safety and improves the driving experience. This study proposes a novel data and model hybrid-driven fine-grained longitudinal driving intention prior recognition system (LDIPRS) that integrates the human-pedal interaction sensor (HPIS) based on triboelectric nanogenerators (TENG) and the channel attention (CA)-enhanced convolutional neural network (CBRCNet). The HPIS is integrated into the vehicle's pedals to respond to and monitor the driver's longitudinal driving behavior in advance. The CBRCNet outperforms others compared baselines (LSTM, Bi-LSTM, CNN, and TF) achieving 98.8 % intention recognition accuracy 300 ms after the HPIS response, and 96.1 % recognition accuracy after 50 ms. As a result, our proposed LDIPRS realizes the recognition of emergency braking, rapid acceleration, normal braking, and normal acceleration in advance by 732 ms, 1035 ms, 1757 ms, and 2227 ms, compared to the driving simulator used in this paper. This study introduces self-powered, low-cost, highly sensitive triboelectric sensors into the field of intention recognition, and combines the triboelectric sensors with deep learning algorithms to provide a promising solution to improve the safety of autonomous vehicles and the efficiency of intelligent transportation systems. • Realizing the driver’s maneuver monitoring without interference. • Identifying the driver’s intention in advance with deep learning model. • Improving the safety of autonomous vehicles and the efficiency of transportation systems.