Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst case characterization gives a conservative uncertainty prediction and may result in infeasible motion planning for the ego robotic system. In this article, an efficient, robust, and safe motion-planning algorithm is developed by learning the obstacle uncertainties online. More specifically, the unknown yet intended control set of obstacles is efficiently computed by solving a linear programming (LP) problem. The learned control set is used to compute forward reachable sets (FRSs) of obstacles that are less conservative than the worst case prediction. Based on the forward prediction, a robust model predictive controller is designed to compute a safe reference trajectory for the ego robotic system that remains outside the reachable sets of obstacles over the prediction horizon. The method is applied to a car-like mobile robot in both simulations and hardware experiments to demonstrate its effectiveness.

This article proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.

The potential of additional steering possibilities (like rear-wheel or all-wheel steering) is analyzed for critical situations to investigate possible safety improvements. For this purpose, a dynamic optimization problem is formulated to find the best possible maneuver. The optimization criterion is to maximize the entry speed into a constant radius -curve. The optimization problem is solved for different steering topologies, and the results quantify the increase in maximum entry speed, highlighting the potential for safety improvements. Further, the optimal steering strategies are determined, and they show interesting characteristics like initial diagonal driving or, in other cases, a transition from initial out-of-phase steering to in-phase steering.

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users’ future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.

Avoidance maneuvers at normal driving speed or higher are demanding driving situations that force the vehicle to the limit of tire-road friction in critical situations. To study and develop control for these situations, dynamic optimization has been in growing use in research. One idea to handle such optimization computations effectively is to divide the total maneuver into a sequence of sub-maneuvers and to associate a segmented optimization problem to each sub-maneuver. Here, the alternating augmented Lagrangian method is adopted, which like many other optimization methods benefits strongly from a good initialization, and to that purpose a method with motion candidates is proposed to get an initially feasible motion. The two main contributions are, firstly, the method for computing an initially feasible motion that is found to use obstacle positions and progress of vehicle variables to its advantage, and secondly, the integration with a subsequent step with segmented optimization showing clear improvements in paths and trajectories. Overall, the combined method is able to handle driving scenarios at demanding speeds.

An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle.

To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified and presented using sensitivity analysis and closed-loop simulations. The study shows that different model parameters are more or less critical depending on the investigated scenario.- Using sampling-based closed-loop predictions, uncertainty bounds on state variable trajectories are determined. Our findings suggest the potential for the inclusion of our method within a robust predictive controller or as a driver-assistance system for rollover or lane departure warning.

Handling of critical situations is an important part in the architecture of an autonomous vehicle. A controller for autonomous collision avoidance is developed based on a wary strategy that assumes the least tireroad friction for which the maneuver is still feasible. Should the friction be greater, the controller makes use of this and performs better. The controller uses an acceleration-vector reference obtained from optimal control of a friction-limited particle, whose applicability is verified by using numerical optimization on a full vehicle model. By employing an analytical tire model of the tireroad friction limit, to determine slip references for steering and body-slip control, the result is a controller where the computation of its output is explicit and independent of the actual tire-road friction. When evaluated in real-time on a high-fidelity simulation model, the developed controller performs close to that achieved by offline numerical optimization.

A controller for critical vehicle maneuvering is proposed that avoids obstacles and keeps the vehicle on the road while achieving heavy braking. It operates at the limit of friction and is structured in two main steps: a motion-planning step based on receding-horizon planning to obtain acceleration-vector references, and a low-level controller for following these acceleration references and transforming them into actuator commands. The controller is evaluated in a number of challenging scenarios and results in a well behaved vehicle with respect to, e.g., the steering angle, the body slip, and the path. It is also demonstrated that the controller successfully balances braking and avoidance such that it really takes advantage of the braking possibilities. Specifically, for a moving obstacle, it makes use of a widening gap to perform more braking, which is a clear advantage of the online replanning capability if the obstacle should be a moving human or animal. Finally, real-time capabilities are demonstrated. In conclusion, the controller performs well, both from a functional perspective and from a real-time perspective.

Autonomous vehicle functions in safety-critical situations show promise in reducing the risk and saving lives in accidents compared to existing safety systems. Consequently, it is from many perspectives advantageous to be able to quantify the potential benefits of new autonomous systems for vehicle maneuvers at-the-limit of tire friction. Here, to estimate the potential in terms of saved lives and reduced degree of injuries in accidents for new, not yet existing systems, a framework has been developed by combining available historic data, in the form of crash databases, and statistical methods with comparative calculations of vehicle behavior using numerical optimization rather than simulation. The framework performs effectively, it gives interesting insights into the relation between more traditional active yaw control and optimal autonomous lane-keeping control, and it clearly demonstrates the potential of saved lives by using autonomous vehicle maneuvers.