Risk-Aware Lane Selection on Highway with Dynamic Obstacles
This work addresses lane selection for autonomous vehicles on highways, but it is incremental as it builds on existing motion planning frameworks.
The paper tackles the problem of discretionary lane selection on highways by proposing a real-time algorithm that evaluates uncertain dynamic positions of surrounding vehicles to optimize travel time and safety. It demonstrates the algorithm using a state-of-the-art motion planner in a CARLA simulation environment.
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.