  This paper proposes a discretionary lane selection algorithm. In particular,
highway driving is considered as a targeted scenario, where each lane has a
different level of traffic flow. When lane-changing is discretionary, it is
advised not to change lanes unless highly beneficial, e.g., reducing travel
time significantly or securing higher safety. Evaluating such "benefit" is a
challenge, along with multiple surrounding vehicles in dynamic speed and
heading with uncertainty. We propose a real-time lane-selection algorithm with
careful cost considerations and with modularity in design. The algorithm is a
search-based optimization method that evaluates uncertain dynamic positions of
other vehicles under a continuous time and space domain. For demonstration, we
incorporate a state-of-the-art motion planner framework (Neural Networks
integrated Model Predictive Control) under a CARLA simulation environment.
