Since the introduction of hands-free telephony applications and speech dialog systems in automotive industry in 1990s, microphones have been mounted in car cabins to capture, and route the driver's speech signals to the corresponding telecommunication networks. A car cabin is a noisy and reverberant environment where engine activity, structural vibrations, road bumps, and cross-talk interferences can add substantial amounts of acoustic noise to the captured speech signal. To enhance the speech signal, a variety of real-time signal enhancement methods such as acoustic echo cancellation, noise reduction, de-reverberation, and beamforming are typically applied. Moreover, the recent introduction of AI-driven online voice assistants in automotive industry has resulted in new requirements on speech signal enhancement methods to facilitate accurate speech recognition. In this chapter, we focus on spatial filtering techniques that are designed to spatially enhance signals that arrive from certain directions while attenuating signals that originate from other locations. The fundamentals of conventional beamforming and echo cancelation are explained and are accompanied by some real-world examples. Moreover, more recent techniques (namely blind source segregation, and neural-network based adaptive beamforming) are presented in the context of automotive applications. This chapter provides the readers with both fundamental and hands-on insights into the fast-growing field of automotive speech signal processing.