Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object generates enormous measurements targets extended positions using light detection ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because dynamic data association uncertainties during scene understandin...

The ability to predict the motion of vehicles is essential for autonomous vehicles. Aiming at problem that existing models cannot make full use external parameters including outline and lane, we proposed a model thoroughly when predicting trajectory in straight-line non-free flow state. Meanwhile, dynamic sensitive area filter out inconsequential surrounding historical their are used as inputs....

Trajectory prediction of surrounding vehicles is a crucial capability intelligent driving vehicles. In scene, vehicle and its constitute an integral system, the vehicle's future motion trajectory affected by actions The influencing mode degree are hidden in relevant historical information neighbour vehicle. existing methods either do not consider confidence predicted trajectory, or accuracy req...

This paper describes some of the interactions of model learning algorithms and planning algorithms we have found in exploring model-based reinforcement learning. The paper focuses on how local trajectory optimizers can be used effectively with learned nonparametric models. We find that trajectory planners that are fully consistent with the learned model often have difficulty finding reasonable ...

This paper focuses on the trajectory planning and tracking control of articulated tracked vehicles (ATVs). It utilizes path method based Hybrid A-star minimum snap smoothing to obtain feasible kinematic trajectory. To overcome highly non-linearity ATVs, we proposed a linear-parameter-varying (LPV) tracking-error model. Then, controller was formulated as adaptive model predictive (AMPC). The sim...