Are there any machine learning techniques to identify points on plots/ images?
A first derivative, on the surface, would do it.  However, the data you show have a great deal of noise in them, so we need a way to evaluate the first derivative in a somewhat noiseless way, or at least within a frequency domain that eliminates the jitter and preserves the major derivative change.

Wavelet analysis could achieve this for you, especially if you use the first derivative of a Gaussian as your mother wavelet.  R has some decent wavelet packages (see r-project.org for starters).  If you do the wavelet transform at short scales, this will identify the locations of the bits of jitter in the steering.  If you do it at larger scales (i.e. lower frequency), you can likely find just the lane shifts and not the little jitters.

If you train the transform up with a reasonable data set, you should be able to identify a scale or range of scales that corresponds to lane changes.  But note that if you don't figure that out, this is something like O(n^2), so try to narrow the scale range down a bit to save compute time.