Based　on　parameter　prediction,　a　RBF　neural　network　adaptive　control　scheme　was　proposed　for　the　motion　control　problems　of　autonomous　electric　vehicles　with　uncertainties.　Firstly,　the　influences　of　system　parameter　uncertainties　and　external　interferences　were　considered,　and　a　dynamic　model　which　might　reflect　the　tracking　and　following　behaviors　of　vehicles　was　established　by　the　preview　method.　Secondly,　RBF　neural　network　compensator　was　adopted　to　compensate　system　uncertainties　adaptively,　and　a　generalized　coordinated　control　law　was　designed　for　the　lateral　and　longitudinal　motions　of　vehicles.　Thirdly,　the　impacts　from　the　front　vehicle　speeds　and　road　curvatures　were　taken　into　account,　and　the　minimization　of　the　energy　consumption　and　the　average　jerks　in　the　tracking　and　following　control　processes　were　regarded　as　the　optimization　objects.　Afterwards,　PSO　algorithm　was　utilized　to　rolling　optimize　the　gain　parameter　K　in　the　coordinated　control　law,　and　then　a　series　of　optimized　sample　data　were　obtained.　Then,　to　ensure　the　economy　and　ride　comfort　of　vehicles,　a　BP　neural　network　was　designed　and　trained　to　realize　the　real-time　prediction　of　gain　parameter　K　in　the　generalized　coordinated　control　law.　Simulation　results　validate　the　effectiveness　of　the　proposed　control　scheme.　©　2024　Chinese　Mechanical　Engineering　Society.　All　rights　reserved.