It is convenient to consider this problem in a probabilistic setting. Let $U_1,U_2,\ldots$ be independent uniform$[0,1]$ random variables. Then, your function $A(x)$ (supported on $[-1,1]$) is the probability density function (pdf) of the random variable $X = U_1 + U_2 - 1$; see, page 17 here (notes from Purdue university) for this easy fact. Thus, the convolution of $A$ with itself gives the pdf of the sum $X_1 + X_2$, where $X_1$ and $X_2$ are independent copies of $X$, hence the pdf of the random variable $U_1+U_2+U_3+U_4-2$ (which is supported on $[-2,2]$). According to MathWorld, the pdf of $U_1+\cdots+U_4$ is given by
$$
f_{U_1 + \cdots + U_4 } (x) = \frac{1}{{2(4 - 1)!}}\sum\limits_{k = 0}^4 {( - 1)^k {4 \choose k}(x - k)^{4 - 1} {\mathop{\rm sgn}} (x - k)} , \;\; x \in [0,4].
$$
(Replace $4$ with $n$ for the pdf of $U_1+\cdots+U_n$.) It thus follows that
$$
(A * A)(x) = f_{U_1 + \cdots + U_4 } (x+2),\;\; x \in [-2,2],
$$
where $*$ denotes convolution. (Note that $A$ is a symmetric function.) I have verified this result by comparing to the right-hand side of
$$
(A * A)(x) = \int_{ - 1}^1 {A(x - y)A(y)\,{\rm d}y} , \;\; x \in [-2,2]
$$
(recall the definition of convolution, and note that $A * A$ is supported on $[-2,2]$).
For example, the following values were obtained for $x=-1.65$ and $x=1.5$, respectively:
$$
0.0071458333333343435 , 0.007145833333334295
$$
and
$$
0.020833333333335497 , 0.020833333333333332,
$$
where the left-hand values are approximations of the above integral defining $(A*A)(x)$.