Chapter 3.2: Genetic Algorithm
Key Contributions
- The design of a GA which gives results within low computational times for traffic scenarios.
- Employment of the developed GA for constant path adaptation to overcome actuation uncertainties. The GA assesses the current scenario and takes the best measures for rapid trajectory generation.
- The use of traffic rules as heuristics to coordinate between vehicles.
- The use of heuristics for constant adaptation of the plan to favour overtaking, once initiated, but to cancel it whenever infeasible.
- The approach is tested for a number of diverse behaviours including obstacle avoidance, blockage, overtaking and vehicle following.
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Chapter 3.3: Rapidly-exploring Random Trees
Key Contributions
- Inspired by the general motion of vehicles in traffic, a planning strategy is proposed which is biased towards a vehicle’s current lateral position. This enables better tree expansion and connectivity checks.
- RRT generation is integrated with spline based curve generation for curve smoothing.
- The approach is designed and tested for many and complex obstacles in the presence of multiple vehicles.
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Chapter 3.4: Rapidly-exploring Random Trees Connect
Key Contributions
- The planning algorithm can be used with very low computational requirements for very simple behaviours, while higher computation may enable near-optimal performance.
- A decision making module is proposed for choosing between vehicle following and overtaking behaviours. The module relies on a fast planning lookup.
- The algorithm uses the notion of first building an approximate path and then optimizing it which induces an iterative nature to the algorithm, unlike the standard RRT approaches which invest computation to build a precise path.
- The algorithm uses multiple RRT instances to be assured of being near global optima, which is largely possible due to the fast approximate path construction.
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Chapter 4.2-4.3: Multi-Layer Planning
Key Contributions
- To propose a general planning hierarchy in an assumed complex modelling scenario, where any algorithm may be used at any level of hierarchy.
- To use simple heuristics such as separation maximization, vehicle following and overtaking, to plan the trajectories of multiple vehicles in real time.
- An emphasis is placed on the width of feasible roads as an important factor in the decision making process.
- The developed coordination strategy is largely cooperative, at the same time ensuring near-completeness of the resultant approach and being near-optimal for most practical scenarios.
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Chapter 4.4-4.6: Dynamic Distributed Lanes
Key Contributions
- The generalized notion of lanes is defined.
- The generalized notion is used for planning and coordination of multiple vehicles.
- A pseudo centralized coordination technique is designed which uses the concepts of decentralized coordination for iteratively planning different vehicles but empowers a vehicle to move around the other vehicles. The coordination is hence better in terms of optimality and completeness than most approaches (discussed so far) while being somewhat computationally expensive in the worst cases.
- The concept of one vehicle waiting for another vehicle coming from the other direction is introduced, when there may be space enough for only one vehicle to pass.
- Heuristics are used for pruning the expansions of states, which result in a significant computational efficiency while leading to a slight loss of optimality.
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