This thesis introduces two improvements in trajectory planning on uneven terrain with dynamic obstacles: the Dynamic Traversability-based Hybrid A* (DT Hybrid A*) algorithm and the Safety Direction Velocity Obstacle (DSVO) replanning method. The DT Hybrid A* differs from traditional path-planning approaches by adopting a less conservative strategy, incorporating vehicle momentum into terrain traversability in order to more accurately reflect the vehicle’s capability to navigate uneven terrain. Based on dynamic traversability, the DT Hybrid A* is developed by leveraging a kinematic bicycle model for steering, allowing variable travel lengths between adjacent nodes, and incorporating both distance and time heuristics. These enhancements significantly reduce CPU time costs during the planning process, resulting in shorter path lengths and faster planning times. This thesis incorporates a collision detection mechanism within the global path planning framework, enabling the system to replan if a potential collision is predicted in the future. The replanning process initializes the global path to avoid all static obstacles. With the movement of dynamic obstacles, if a potential conflict is recognized, the vehicle discards the current global path and replans from its current location. To ensure that the replanned path is free of dynamic obstacles, the collision detection mechanism incorporates Safety and Direction Elements based on a Velocity Obstacle (VO) cost term into the global path algorithm (DSVO). The DSVO algorithm enhances collision avoidance by integrating Safety and Direction Elements based on a VO cost term, surpassing traditional methods that use Euclidean distance penalties. DSVO avoids collisions by considering obstacle velocity and direction over time. It enhances the rate of successful replans for dynamic obstacle avoidance, producing the shortest collision-free paths with minimal computational overhead and completion time. The effectiveness of these approaches highlights the balance between computational efficiency and dynamic obstacle management, paving the way for more reliable path replanning solutions. DSVO surpasses methods that use Euclidean distance as a penalty for dynamic obstacles because it considers the obstacle’s velocity and direction over time. It also outperforms the Safety Element based on a VO cost term, which only ensures collision avoidance within a short time frame and provides the optimal velocity for that period but does not account for longer-term safety, potentially leading to collisions over extended durations. Additionally, since these are cost terms within global path planning algorithms, there can be conflicts between obstacle avoidance and achieving the goal. The DSVO algorithm, which incorporates Safety and Direction Elements based on a VO cost term, mitigates conflicts between obstacle avoidance and goal achievement. Additionally, DSVO effectively adjusts the path to navigate around dynamic obstacles while maintaining a direct route toward the goal, balancing safety and efficiency.

As Autonomous Vehicles (AVs) navigate through dynamic and constantly changing environments, it is crucial that they take into account the impact of their actions on the decisions of others for safe and efficient interaction with humans. In doing so, they need to anticipate how humans will behave in different situations based on their intentions. This work aims to address these challenges by proposing an expressive game-theoretic framework for modeling the interactions between AVs and human drivers as a multi-agent dynamic game, in which each agent seeks to optimize their respective objective. The optimization problem is solved by obtaining the Nash equilibrium, which accounts for the potential non-cooperative behavior of human drivers. To incorporate the notion of intention into the game-theoretic formulation, we introduce for each agent a parameter known as Social Value Orientation (SVO), reflecting the degree to which an agent is willing to prioritize the welfare of others over its own. We then develop efficient methods to solve this nonlinear optimization problem in a receding-horizon fashion given the agents' SVOs. However, cluttered traffic scenarios are typically characterized by uncertainty regarding the intentions of other traffic participants due to noisy sensor data, and multiple equally admissible equilibrium strategies that humans may adapt to achieve their objective. Therefore, an approximate Bayesian inference method is developed to infer the intentions of the surrounding human participants by estimating the likelihood of SVO based on newly received state observations. We then integrate the estimation module into the game-theoretic planning module in a combined framework and evaluate its predictive performance against algorithms that ignore these sources of uncertainty in two simulated traffic scenarios; ramp-merging at a highway and crossing at uncontrolled intersections. Our results show that the proposed inference method exhibits superior performance compared to all other approaches, with the average prediction error approaching zero. This implies that dynamically changing the SVO values, while planning, effectively captures the true intentions of the surrounding agents.

Automated driving is where automobiles meet robotics. With the recent advances in intelligence, sensor technology, wireless technology, and computation power, we are inching ever closer to realising full autonomy in a vehicle. We are nowhere near the end of the line, however. Automated vehicles will have to interact with static obstacles like pavements, dividers, and poles and dynamic elements like pedestrians and other vehicles. This opens up a range of sub-topics on privacy, safety, and decision-making. While driving on the road in the presence of other agents(human or autonomous), safety constraints and collision avoidance are of paramount importance. Even with this achieved, we need a good performance in the latency of processing each iteration; we need constraints to be followed and, of course, ensure minimal errors in our control.

Through this thesis, we introduce automated driving, its general pipeline stack, and industry standards for autonomy. We then get ourselves up-to-date with the latest trends and advances in motion planning, decision-making, and control of automated vehicles. Once that is covered, we narrow our focus towards using a scenario-based approach to safe trajectory planning. We delve in-depth into safety constraints guaranteed by this approach and discuss previous results obtained by using this method with pedestrians in an urban setting.

This thesis aims to extend this previously used scenario-based planning method to a multi-vehicle implementation. Two methods of modelling scenario distributions(assumed to be Gaussian) are proposed, implemented, and compared using evaluation metrics like computation times, safety, and time taken to reach the goal. The first method models each obstacle vehicle as a series of obstacles linked together by the vehicle constraints. Thus, each vehicle is represented by multiple collision regions corresponding to each obstacle. The second method models the vehicle as having a single collision region with multiple scenario distributions. This extension's safety/risk guarantee is shown both theoretically and experimentally. Experiments are conducted in simulation on an urban straight road and at a T-Junction with multiple obstacle vehicles, and performances are compared, not only between these two methods but also with each obstacle modelled as a single disc, which is the baseline implementation. Conclusions are then made based on the performance metrics, and further improvements are proposed. It is shown that modelling the vehicle as a series of linked scenarios improves over the baseline method and the multiple obstacle discs implementation in terms of safety and computation times respectively.