Program: 189597
Project: 189597
Buggy source:
/** 
 * Pass it a road-coordinates prior predictive belief distribution, edge and  path starting distance, and it will update the prior predictive distribution  for that edge and path.  Otherwise, project free-movement onto an edge or  predict free movement.
 * @param belief
 * @param edge
 * @param startOfEdgeDist 
 */
public void predict(MultivariateGaussian belief,PathEdge edge,PathEdge prevEdge){
  Preconditions.checkArgument(belief.getInputDimensionality() == 2 || belief.getInputDimensionality() == 4);
  if (edge == PathEdge.getEmptyPathEdge()) {
    if (belief.getInputDimensionality() == 4) {
      groundFilter.predict(belief);
    }
 else {
      Preconditions.checkNotNull(prevEdge);
      convertToGroundBelief(belief,prevEdge);
      groundFilter.predict(belief);
      if (belief.getMean().getElement(0) > prevEdge.getDistToStartOfEdge() + prevEdge.getInferredEdge().getLength()) {
        belief.getMean().setElement(0,prevEdge.getDistToStartOfEdge() + prevEdge.getInferredEdge().getLength());
      }
 else       if (belief.getMean().getElement(0) < prevEdge.getDistToStartOfEdge()) {
        belief.getMean().setElement(0,prevEdge.getDistToStartOfEdge());
      }
      Preconditions.checkArgument(belief.getMean().getElement(0) >= 0);
    }
  }
 else {
    if (belief.getInputDimensionality() == 4) {
      invertProjection(belief,edge);
      roadFilter.predict(belief);
    }
 else {
      if (edge == null) {
        roadFilter.predict(belief);
      }
 else {
        final double S=Or.times(belief.getCovariance()).times(Or.transpose()).getElement(0,0) + Math.pow(edge.getInferredEdge().getLength() / Math.sqrt(12),2);
        final Matrix W=belief.getCovariance().times(Or.transpose()).scale(1 / S);
        final Matrix R=belief.getCovariance().minus(W.times(W.transpose()).scale(S));
        final double mean=(edge.getDistToStartOfEdge() + edge.getInferredEdge().getLength()) / 2d;
        final double e=mean - Or.times(belief.getMean()).getElement(0);
        final Vector a=belief.getMean().plus(W.getColumn(0).scale(e));
        Preconditions.checkArgument(a.getElement(0) >= 0);
        belief.setMean(a);
        belief.setCovariance(R);
      }
    }
  }
}

Fixed version (ground truth):
/** 
 * Pass it a road-coordinates prior predictive belief distribution, edge and  path starting distance, and it will update the prior predictive distribution  for that edge and path.  Otherwise, project free-movement onto an edge or  predict free movement.
 * @param belief
 * @param edge
 * @param startOfEdgeDist 
 */
public void predict(MultivariateGaussian belief,PathEdge edge,PathEdge prevEdge){
  Preconditions.checkArgument(belief.getInputDimensionality() == 2 || belief.getInputDimensionality() == 4);
  if (edge == PathEdge.getEmptyPathEdge()) {
    if (belief.getInputDimensionality() == 4) {
      groundFilter.predict(belief);
    }
 else {
      Preconditions.checkNotNull(prevEdge);
      convertToGroundBelief(belief,prevEdge);
      groundFilter.predict(belief);
    }
  }
 else {
    if (belief.getInputDimensionality() == 4) {
      invertProjection(belief,edge);
      roadFilter.predict(belief);
    }
 else {
      if (edge == null) {
        roadFilter.predict(belief);
      }
 else {
        final double S=Or.times(belief.getCovariance()).times(Or.transpose()).getElement(0,0) + Math.pow(edge.getInferredEdge().getLength() / Math.sqrt(12),2);
        final Matrix W=belief.getCovariance().times(Or.transpose()).scale(1 / S);
        final Matrix R=belief.getCovariance().minus(W.times(W.transpose()).scale(S));
        final double mean=(edge.getDistToStartOfEdge() + edge.getInferredEdge().getLength()) / 2d;
        final double e=mean - Or.times(belief.getMean()).getElement(0);
        final Vector a=belief.getMean().plus(W.getColumn(0).scale(e));
        Preconditions.checkArgument(a.getElement(0) >= 0);
        belief.setMean(a);
        belief.setCovariance(R);
      }
    }
  }
}