Numerous researchers are committed to finding solutions to the path planning
problem of intelligence-based vehicles. How to select the appropriate algorithm
for path planning has always been the topic of scholars. To analyze the
advantages of existing path planning algorithms, the intelligence-based vehicle
path planning algorithms are classified into conventional path planning methods,
intelligent path planning methods, and reinforcement learning (RL) path planning
methods. The currently popular RL path planning techniques are classified into
two categories: model based and model free, which are more suitable for complex
unknown environments. Model-based learning contains a policy iterative method
and value iterative method. Model-free learning contains a time-difference
algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA)
algorithm, and Monte Carlo (MC) algorithm. Then, the path planning method based
on deep RL is introduced based on the shortcomings of RL in intelligence-based
vehicle path planning. Finally, we discuss the trend of path planning for
vehicles.
