The development and popularity of vehicle-to-everything communication have caused more risks to the in-vehicle networks security. As a result, an increasing number of various and effective intrusion detection methods appear to guarantee the security of in-vehicle networks, especially deep-learning-based methods. Nevertheless, the state-of-the-art deep-learning-based intrusion detection methods lack a quantitative and fair horizontal performance comparison analysis. Also, they have no comparative analysis of the detection capability for the unknown attacks as well as on the time and hardware resource consumption of their intelligent intrusion detection models. Therefore, this paper investigates ten representative advanced deep-learning-based intrusion detection methods and illustrates the characteristics and advantages of each method. Moreover, quantitative and fair experiments are set to make horizontal comparison analyses. Also, this study provides some significant suggestions on baseline method selection and valuable guidance, for the direction of future research about lightweight models and the ability to detect unknown attacks.