Yes, I believe there is. Take a look at this answer, which is similar, but does not use the caret library. If you add svp = fit$finalModelto the example, you should be able to follow it almost exactly. I applied a similar technique to your data below. I scaled the data to fit nicely on the plot of the vectors since the library scales the data at runtime.

require(caret)

set.seed(1)
x = model.matrix(data=mtcars, mpg ~ scale(hp)) #set up data
y = mtcars$mpg

fit=train(x, y, method="svmLinear") #train

svp = fit$finalModel #extract s4 model object

plot(x, xlab="", ylab="")
w <- colSums(coef(svp)[[1]] * x[unlist(alphaindex(svp)),])
b <- b(svp)
abline(b/w[1],-w[2]/w[1], col='red')
abline((b+1)/w[1],-w[2]/w[1],lty=2, col='red')
abline((b-1)/w[1],-w[2]/w[1],lty=2, col='red')

And your second question:

x = model.matrix(data=mtcars, mpg ~ scale(hp) + scale(wt) - 1) #set up data
fit=train(x, y, method="svmLinear") #train

svp = fit$finalModel #extract s4 model object

plot(x, xlab="", ylab="")
w <- colSums(coef(svp)[[1]] * x[unlist(alphaindex(svp)),])
b <- b(svp)
abline(b/w[1],-w[2]/w[1], col='red')
abline((b+1)/w[1],-w[2]/w[1],lty=2, col='red')
abline((b-1)/w[1],-w[2]/w[1],lty=2, col='red')

Edit

The above answer concerns plotting a boundary, not the linear SVM regression line. To answer the question, one easy way to get the line is to extract the predicted values and plot the regression. You actually only need a couple of points to get the line, but for simplicity, I used the following code.

abline(lm(predict(fit, newdata=mtcars) ~ mtcars$hp), col='green')

or

abline(lm(predict(fit) ~ mtcars$hp), col='green')