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Disengagement cases during naturalistic driving are rare or even one-shot, but valuable for autonomous driving. The autonomous vehicles are necessary to continually learn from these disengagement cases, to improve the policy for better performance when next time meeting these cases. Manually adjusting the policy or adding the rules to fix these disengagement cases may cause engineering burden and may contradict other driving functions. To this end, this work proposes a continually learning agent which can automatically get improved once encountering a disengagement case. The main idea is to establish a disengagement-imagination environment, and then train the policy using imagination data for performance improvement, named disengagement-case imagination augmented continual learning (DICL). In the imagination environment, the surrounding objects are designed to first follow the recorded …