To　date,　commercial　fully　autonomous　driving　is　not　realized,　while　level　3　is　the　next　step　in　the　development　of　autonomous　driving.　At　level　3,　the　vehicle　is　driving　under　the　control　of　the　machine,　but　when　feature　requests,　human　driver　must　take　over　control.　Therefore,　autonomous　driving　control　should　consider　not　only　efficiency　and　safety　but　also　human　driver＇s　acceptance.　This　paper　develops　a　car-following　(CF)　model　as　a　longitudinal　control　strategy　for　level　3　autonomous　driving　based　on　the　automating　entropy　adjustment　on　Tsallis　actor-critic　(ATAC)　algorithm.　1641　pairs　of　CF　trajectories　extracted　from　the　Next　Generation　Simulation　(NGSIM)　data　are　applied　to　train　the　reinforcement　learning　(RL)　agent.　Based　on　the　empirical　data　distributions,　we　use　time　margin,　time　gap,　and　jerk　to　construct　the　reward　function　and　testify　the　proposed　CF　model＇s　merits.　Simulation　results　show　that　the　proposed　model　can　enable　vehicles　to　drive　safely,　efficiently,　and　comfortably.　The　proposed　model　has　good　stability,　and　the　generated　driving　behaviors　are　more　acceptable　for　drivers.　This　work　sheds　light　on　developing　a　better　autonomous　driving　system　from　the　perspective　of　human　factors.