  Reinforcement learning has emerged as an important approach for autonomous
driving. A reward function is used in reinforcement learning to establish the
learned skill objectives and guide the agent toward the optimal policy. Since
autonomous driving is a complex domain with partly conflicting objectives with
varying degrees of priority, developing a suitable reward function represents a
fundamental challenge. This paper aims to highlight the gap in such function
design by assessing different proposed formulations in the literature and
dividing individual objectives into Safety, Comfort, Progress, and Traffic
Rules compliance categories. Additionally, the limitations of the reviewed
reward functions are discussed, such as objectives aggregation and indifference
to driving context. Furthermore, the reward categories are frequently
inadequately formulated and lack standardization. This paper concludes by
proposing future research that potentially addresses the observed shortcomings
in rewards, including a reward validation framework and structured rewards that
are context-aware and able to resolve conflicts.
