  Reinforcement learning (RL) based autonomous driving has emerged as a
promising alternative to data-driven imitation learning approaches. However,
crafting effective reward functions for RL poses challenges due to the
complexity of defining and quantifying good driving behaviors across diverse
scenarios. Recently, large pretrained models have gained significant attention
as zero-shot reward models for tasks specified with desired linguistic goals.
However, the desired linguistic goals for autonomous driving such as "drive
safely" are ambiguous and incomprehensible by pretrained models. On the other
hand, undesired linguistic goals like "collision" are more concrete and
tractable. In this work, we introduce LORD, a novel large models based opposite
reward design through undesired linguistic goals to enable the efficient use of
large pretrained models as zero-shot reward models. Through extensive
experiments, our proposed framework shows its efficiency in leveraging the
power of large pretrained models for achieving safe and enhanced autonomous
driving. Moreover, the proposed approach shows improved generalization
capabilities as it outperforms counterpart methods across diverse and
challenging driving scenarios.
