Motion Planning for Multiple Autonomous Vehicles Fuzzy Logic Rahul Kala Presentation of paper: R. Kala, K. Warwick (2015). Reactive Planning of Autonomous Vehicles for Traffic Scenarios. Electronics 4, 739-762 April, 2013

Why Fuzzy Logic? Computational Time Work with partially known environments Issues Completeness Optimality

Key Contributions Design of a Fuzzy Inference System for the problem. Design of a decision making module for deciding the feasibility of overtaking purely based on the vehicle distances and speeds. Design of an evolutionary technique for optimization of such a fuzzy system. Using the designed fuzzy system enabling vehicles to travel through a crossing by introducing a virtual barricade.

Fuzzy Inference System Codify the immediate scenario to a few inputs. Decide the actions to be taken and hence design the outputs. Think of various scenarios and the associated inputs/outputs and generalize them as rules Design Methodology Formulate Inputs Formulate Outputs Steering Speed Design Rules

Discrete Valued/ Strategy Inputs Continuous Valued Angle deviation from road Distance from left boundary/ obstacle Distance from right boundary/ obstacle Distance from front vehicle/ boundary/ obstacle Side: distance of vehicle in wrong side Discrete Valued/ Strategy Inputs Turn to avoid obstacle/ overtake vehicle in front Requested turn: turn to enable another vehicle to overtake

Minimal of twin distance inputs is taken Some Inputs γi θi Obstacle Left Distances Right Distances Front Distances Deviation = γi – θi Minimal of twin distance inputs is taken

Turn to avoid obstacle If front distance from left corner is less than front distance from right corner, turn right; and vice versa Heuristic holds from most small obstacles/general scenarios

Overtaking Check if the front vehicle (1) is slower and needs to be overtaken by the vehicle being planned (2) Check if road is wide enough to safely accommodate both (1) and (2) Check if overtaking is feasible with any other vehicle (3) on the road while (2) overtakes (1) Initiate the overtake (2) decides the side of overtake (2) steers on decided side (1) steers on opposite side Check whether cooperation of (3) is required or not In case yes, (3) steers depending upon the scenario

Feasibility criterion with 3 vehicles Assumption: Road not wide enough for multiple (>3) vehicles to lie side-by-side The vehicles are projected to travel straight subsequently (else would require to adjust for overtaking) Condition 1: (1), (2) and (3) can simultaneously lie side by side along the road, OR Condition 2: (2) can complete overtake of (1) within the time (3) does not lie in the overtaking zone

Cooperation Whether (3) needs to steer in a particular direction to enable overtake? Cooperation required in case the vehicles need to align to fit within road width (condition 1) (3) moves opposite to the location of (1) and (2)

Vehicle Following In case of infeasibility, the vehicle would follow the vehicle in front If any oncoming vehicle/any other vehicle causing overtake infeasibility passes, overtake initiates Vehicle can drive in the wrong side and does not consider the vehicle in front (both inputs disabled) When (2) is ahead of (1), the inputs are enabled again

Crossing Add virtual barricade as boundaries on road not used in navigation Make the vehicle go by designed fuzzy planned – overtaking disabled In absence of traffic lights first come first serve sequence followed

Crossing Left Boundary Right Boundary Barricades

Evolution of fuzzy planner Optimization using Genetic Algorithm Complete design using Genetic Algorithm would be computationally expensive Initial fuzzy planner designed by human based on sample scenarios Altering optimization of rules and membership functions for a few cycles Rule optimization can increment/decrement any antecedent/consequent by a unity Membership function optimization can move any membership function parameters within a narrow region. Fitness Function: Minimize time, minimize collisions, minimize time in wrong side, minimize safety distance breach

Evolution of fuzzy planner F ← Human Designed Fuzzy Planner While no of cycles are not met F ← Tune Rules (F) F ← Tune Membership Function Parameters (F) Fitness Evaluation Simulation Map Return F End

Results – Single Vehicle

Results – Vehicle Avoidance

Results – Multi Vehicle

Results – Multi Vehicle

Results - Crossing

Analysis Two vehicle scenarios Single vehicle scenarios

Analysis Overtaking scenarios

Angle Deviation from Road Two vehicle scenarios Single vehicle scenarios

Angle Deviation from Road Overtaking scenarios

Thank You Acknowledgements: Commonwealth Scholarship Commission in the United Kingdom British Council Thank You