A controller area network (CAN) bus protocol is widely used in the vehicular system and an efficient standard bus enabling communication between all electronic control units (ECUs). However, the CAN bus is easy to be attacked because of a lack of security defense features. This paper proposes self-attention mechanism (SAM) enhanced grid long short-term memory (Grid LSTM) for vehicular intrusion detection, namely SALVID. The SAM can enhance the characteristics of CAN bus-oriented attack behavior, and the Grid LSTM can effectively extract the depth features of time series data. This paper generates five attack datasets by extracting benign CAN data from the actual car, including denial of service (DoS), fuzzy, spoofing, replay, and delete attacks. This paper compares the performance of various models with different model depths, and the results demonstrate that SALVID has the best performance in detecting the attacks on CAN Bus. The model can identify attacks with small-batch features according to an overall detection accuracy of 98.98%, which is hard to do in previous studies. This paper also designs and implements SALVID based on field programmable gate array (FPGA) embedded platform and uses parallel optimization and quantification to accelerate the model based on previous experiments. Even with a certain degree of quantification, SALVID still displays high detection accuracy of 98.81% and a latency of 1.88ms. The investigation provides a new idea for designing high-performance and real-time vehicular intrusion detection systems.