A simple example where the kernels are different.
$$A=\begin{pmatrix}1&0\\1&0\end{pmatrix},B=\begin{pmatrix}0&1\\0&1\end{pmatrix}$$ then $$\ker(A+Bt)=\left\langle\begin{pmatrix}t\\-1\end{pmatrix}\right\rangle$$
The rest only covers how to define $v_t$ to be continuous on $t.$
Since $\det (A+tB)=0$ is a polynomial which is zero for infinitely many $t,$ then it must be the zero polynomial.
This means, as a matrix over the field, $K=\mathbb R(t),$ there must be a non-zero vector $\mathbf v(t) \in K^n$ with $(A+Bt)\mathbf v(t)=0.$ We can make the entries of $\mathbf v(t)$ be polynomials (by multiplying by all the denominators,) and then remove common polynomial factors.
So $\mathbf v(t)$ becomes, as a function on $t\in \mathbb R,$ a continuous function which never takes the value $\mathbf 0.$ So you can use: $$v_t=\frac{\mathbf v(t)}{\|\mathbf v(t)\|}.$$
The same argument applies if the matrices are complex, just replacing $\mathbb R$ With $\mathbb C.$
Other incomplete ideas.
The characteristic polynomial of $A+Bt\in M_n(K),$ $$p(\lambda)=\det(\lambda I-(A+Bt))$$ is of the form:
$$\lambda^n +\sum_{k=1}^{n-1}q_k(t)\lambda^k$$ where $\deg q_k\leq n-1.$ We can skip $k=0$ because $\det(A+Bt)=0.$
Thus the minimal polynomial $m(\lambda)=\lambda m_0(\lambda)$ is also of this form, I believe.
Then $m_0(A+Bt)$ has a column space of dimension $1.$ Otherwise, we’d have two linearly independent $\mathbf v_1(t),\mathbf v_2(t)$ and thus two vectors in the kernel, for at least some real $t,$ it would seem.
Can $\mathbf v_i(t)$ be linearly dependent for all $t$ but independent in $K^n?$
We can limit to the cases when $A$ is in Jordan normal form.
In the case of $2\times 2$ matrices, this means you can restrict to $$A_1=\begin{pmatrix}0&0\\0&1\end{pmatrix},\\A_2=\begin{pmatrix}0&1\\0&0\end{pmatrix}$$
Now, let $B=(b_{ij})_{i,j=1}^2.$
The characteristic polynomial for $A_1+Bt$ is $$\lambda^2-(t(b_{11}+b_{22})+1)\lambda +t^2\det B+b_{11}t.$$
So you need $b_{11}=\det B=0.$ This means $B$ must either have a zero first row or zero first column.
If $B$ has a zero first column, then the kernel,s are all the same, generated by $(1,0).$
But in the other case, you need:
$$t(b_{21}x+b_{22}y)+y=0,$$
If $y=t,$ you get:
$$x=\frac{-1-b_{22}t}{b_{21}}.$$
The case for $A_2+Bt$ requires $b_{21}=0$ and $b_{11}b_{22}=0.$
Again, the kernels are equal when $b_{11}=0.$
You get a kernel generated by $y=t, x=-\frac{1+b_{12}t}{b_{11}}.$
So for $2\times 2$ matrices, $\mathbf v(t)$ can be made linear.
You might be able to generate similar examples in $3\times 3$ matrices. I wouldn’t be totally surprised if it was always linear, but it seems unlikely on first blush. It is certainly the case that we can find $\mathbf v(t)$ where each component is of degree at most $n-1.$