Tianyu Gu

Focused Trajectory Planning for autonomous on-road driving

On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.

A behavioral planning framework for autonomous driving

In this paper, we propose a novel planning framework that can greatly improve the level of intelligence and driving quality of autonomous vehicles. A reference planning layer first generates kinematically and dynamically feasible paths assuming no obstacles on the road, then a behavioral planning layer takes static and dynamic obstacles into account. Instead of directly commanding a desired trajectory, it searches for the best directives for the controller, such as lateral bias and distance keeping aggressiveness. It also considers the social cooperation between the autonomous vehicle and surrounding cars. Based on experimental results from both simulation and a real autonomous vehicle platform, the proposed behavioral planning architecture improves the driving quality considerably, with a 90.3% reduction of required computation time in representative scenarios.

Tunable and stable real-time trajectory planning for urban autonomous driving

This paper investigates real-time on-road motion planning algorithms for autonomous passenger vehicles (APV) in urban environments, and propose a computationally efficient planning formulation. Two key properties, tunability and stability, are emphasized when designing the proposed planner. The main contributions of this paper are: · A computationally efficient decoupled space-time trajectory planning structure. · The formulation of optimization-free elastic-band-based path planning and speed-constraint-based temporal planning routines with pre-determined runtime. · Identification of continuity problems with previous cost-based planners that cause tunability and stability issues.

Toward human-like motion planning in urban environments

Prior autonomous navigation systems focused on the demonstration of the technological feasibility. But as the technology evolves, improving user experience through learning experts or individuals driving pattern emerges as a promising research direction. As a first step toward this goal, we investigate methods to learn from human demonstrations in urban scenarios without any environmental disturbances (traffic-free). We propose a path model that generates a reference path with smooth and peak-value-reduced curvature, and a parameterized speed model to be fitted by human driving data. Model parameters are then learned through regression methods, and certain statistical human driving patterns are revealed. The learned model is then evaluated by comparing the generated plan with the collected data by the same human driver.

On-Road Trajectory Planning for General Autonomous Driving with Enhanced Tunability

In order to achieve smooth autonomous driving in real-life urban and highway environments, a motion planner must generate trajectories that are locally smooth and responsive (reactive), and at the same time, far-sighted and intelligent (deliberative). Prior approaches achieved both planning qualities for full-speed-range operations at a high computational cost. Moreover, the planning formulations were mostly a trajectory search problem based on a single weighted cost, which became hard to tune and highly scenario-constrained due to overfitting. In this paper, a pipelined (phased) framework with tunable planning modules is proposed for general on-road motion planning to reduce the computational overhead and improve the tunability of the planner.

Automated tactical maneuver discovery, reasoning and trajectory planning for autonomous driving

In a hierarchical motion planning system for urban autonomous driving, it is a common practice to separate tactical reasoning from the lower-level trajectory planning. This separation makes it difficult to achieve robust maneuver-based tactical reasoning, which is intrinsically linked to trajectory planning. We therefore propose a planning method that automatically discovers tactical maneuver patterns, and fuses pattern reasoning and sampling-based trajectory planning. The results demonstrate enhanced planning feasibility, coherency and scalability.

Runtime-bounded tunable motion planning for autonomous driving

Trajectory planning methods for on-road autonomous driving are commonly formulated to optimize a Single Objective calculated by accumulating Multiple Weighted Feature terms (SOMWF). Such formulation typically suffers from the lack of planning tunability. Two main causes are the lack of physical intuition and relative feature prioritization due to the complexity of SOMWF, especially when the number of features is big. This paper addresses this issue by proposing a framework with multiple tunable phases of planning, along with two novel techniques: Optimization-free trajectory smoothing/nudging. Sampling-based trajectory search with cascaded ranking.