Motion planning plays a significant role in enabling advances of autonomous vehicles in saving lives and improving traffic efficiency. In a predictive motion-planning strategy, the ego vehicle predicts the motion of surrounding vehicles and uses these predictions to plan collision-free reference trajectories. In dynamic multi-vehicle traffic environments, a key research question is how to consider vehicle-to-vehicle interactions and motion uncertainties of the surrounding vehicles in the motion planner to achieve resilient motion planning of the autonomous ego vehicle. 

This Licentiate Thesis proposes a model predictive control (MPC)-based approach to achieve safe motion planning in uncertain and dynamic multi-vehicle driving environments. MPC has been widely applied for the motion planning of autonomous vehicles. However, designing resilient MPC-based motion planners that consider interactions and uncertainties of surrounding vehicles remains an open and challenging problem, which is the primary motivation for the research presented in this thesis. 

This thesis makes several contributions toward solving the interaction and uncertainty-aware motion-planning problems. The first contribution is an MPC, which is called interaction-aware moving target MPC. It is designed based on the combination of an interaction-aware motion-prediction model and time-varying reference targets of the optimal control problem for proactive and non-local trajectory planning in multi-vehicle dynamic scenarios. 

In the second contribution, the proposed MPC is extended to account for the multi-modal motion uncertainties of surrounding vehicles, including the maneuver and trajectory uncertainties, which are predicted by combining an interaction-aware motion-prediction model and a data-driven approach. Based on the modeling of uncertainties, a safety-awareness parameter is included in the design to compute the obstacle occupancy for achieving a trade-off between the performance and robustness of the MPC planner. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset. 

The third contribution of this thesis is quantifying the motion uncertainty of surrounding obstacles to reduce the conservativeness of the motion planner while pursuing robustness. To this end, a robust motion-planning method is designed for robotic systems based on uncertainty quantification of surrounding obstacles. The proposed MPC is called risk-aware robust MPC, as the risk of robustness reduction through uncertainty quantification is analyzed. Simulations in highway merging scenarios of an autonomous vehicle with uncertain surrounding vehicles show that the approach is less conservative than a conventional robust MPC and more robust than a deterministic MPC.  

Rörelseplanering spelar en betydande roll för att möjliggöra framsteg inom autonoma fordon med potential att rädda liv genom att undvika olyckor och förbättra trafikeffektiviteten. I en prediktiv rörelseplaneringsstrategi predikterar det egna fordonet rörelsen hos omgivande fordon och använder dessa prediktioner för att planera en säker trajektoria. I dynamiska trafiksituationer med multipla omgivande fordon är en central forsknings-fråga hur man ska ta hänsyn till de omgivande fordonens interaktioner och rörelseosäkerheter för att åstadkomma en robust rörelseplanering.

Den här licentiatavhandlingen föreslår en modellprediktiv reglerings-ansats (MPC) för rörelseplanering i osäkra och dynamiska flerfordonsmiljöer. Robust och säker modellprediktiv regleringsbaserad rörelseplanering som tar hänsyn till interaktioner och osäkerheter hos rörelsen för omgivande fordon är ett öppet och utmanande problem, vilket är den primära motiveringen för den forskning som presenteras i denna avhandling.

Modellprediktiv reglering (MPC) är en vanlig ansats för rörelseplanering för autonoma fordon. Denna avhandling presenterar metoder som är steg mot att lösa rörelseplaneringsproblemet där interaktion mellan fordon och osäkerhet i rörelser för omgivande fordon beaktas. Det första bidraget fokuserar på interaktionen mellan omgivande fordon. En modellprediktiv regulator har utvecklats baserat på en modell för hur omgivande fordon interagerar och påverkar varandras beteende. Denna modell integreras sedan som tidsvarierande referensmål för det optimala styrningsproblemet vilket ger en förutseende och robust planering för det egna fordonet.

I det andra bidraget utökas den föreslagna MPC-metoden för att ta hänsyn till de multimodala rörelseosäkerheterna hos omgivande fordon; det finns en osäkerhet i vad de omgivande fordonen kommer göra härnäst och det är osäkert hur de kommer genomföra det. Baserat på en modellering av osäkerheterna, en delvis datadriven ansats, inkluderas en säkerhetsparameter i regulatorn som möjliggör en avvägning mellan prestanda och robusthet hos MPC-planeraren.

Den tredje bidraget i avhandlingen är nya metoder för att kvantifiera rörelseosäkerheten hos omgivande fordon och att använda denna för robust planering, utan att det egna fordonet blir för konservativ i sitt agerande. Den föreslagna ansatsen bygger på robust MPC där en riskmedvetenhet introduceras. Simuleringar av motorvägskörning med omgivande fordon med rörelseosäkerhet visar att metoden är mindre konservativ än en konventionell robust MPC och mer robust än en deterministisk MPC.  

In a predictive motion-planning strategy, the autonomous ego vehicle needs to predict the motion of surrounding obstacles and use predictions to plan collision-free reference trajectories. In multi-vehicle traffic environments, a key research question is how to consider vehicle-to-vehicle interactions, behavior uncertainties, and environmental influence on the motion of surrounding obstacles to achieve resilient motion planning of the autonomous ego vehicle. This thesis proposes context-aware motion-planning methods for autonomous driving in uncertain and dynamic environments, and makes several contributions to design motion-planning strategies with the desired performance.

The first contribution is designing an interaction-aware moving target model predictive control (MPC). This method is formulated based on integrating an interaction-aware motion-prediction model and time-varying reference targets of the receding-horizon optimal control problem for proactive and non-local trajectory planning in multi-vehicle dynamic traffic scenarios.

For the second contribution, the proposed interaction-aware moving target MPC planner is extended to account for the multi-modal motion uncertainties of surrounding vehicles, including both the maneuver and trajectory uncertainties. Based on the modeling of uncertainties, a safety-awareness parameter is designed to compute the obstacle occupancy to achieve a trade-off between the performance and robustness of the motion planner.

The third contribution is learning motion uncertainties of obstacles to reduce the conservativeness of the motion planner while pursuing robustness. To this end, a robust motion-planning method is designed for autonomous driving systems based on learning the unknown control set of dynamic obstacles. The learned control set is then applied to predict the reachable set of obstacles to formulate a collision-avoidance constraint. The effectiveness of the method is validated in hardware experiments involving reach-avoid planning problems for mobile robots, and in simulations of autonomous forced merging that incorporates both decision-making and trajectory planning.

The fourth contribution is an environment-aware motion-planning strategy, where the method achieves environment awareness by predicting sensible maneuvers of surrounding vehicles considering road-geometry constraints. By considering environmental factors, the method effectively predicts the forward reachability of surrounding vehicles, which is applied to formulate collision-avoidance constraints in the motion-planning problem. The performance of the proposed strategy is demonstrated through both simulations in roundabout scenarios and hardware experiments with car-like mobile robots.

This thesis demonstrates that autonomous vehicles can perform safe and efficient motion planning under uncertainties based on context awareness.