If the test statistic = approx 2 then then there is no autocorrelation between variables. If <2 or >2 (a lot) then you have a problem (negative or positive autocorrelation). Autocorrelation indicates the order of observations has some effect on the response. Example: If residuals are serially correlated, the order of observations affects the response.
I added a brief explanation of autocorrelation in the context of evaluating residuals because not everyone knows what is meant by autocorrelation and I did not find a concise definition that applies to glm models
