Junqing Wei

Towards a viable autonomous driving research platform

We present an autonomous driving research vehicle with minimal appearance modifications that is capable of a wide range of autonomous and intelligent behaviors, including smooth and comfortable trajectory generation and following; lane keeping and lane changing; intersection handling with or without V2I and V2V; and pedestrian, bicyclist, and workzone detection. Safety and reliability features include a fault-tolerant computing system; smooth and intuitive autonomous-manual switching; and the ability to fully disengage and power down the drive-by-wire and computing system upon E-stop. The vehicle has been tested extensively on both a closed test field and public roads.

A real-time motion planner with trajectory optimization for autonomous vehicles

In this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cost functions. Then an iterative optimization is applied to both the path and speed of the resultant trajectory. The post-optimization is of low computational complexity and is able to converge to a higher-quality solution within a few iterations. Compared with the planner without optimization, this framework can reduce the planning time by 52% and improve the trajectory quality. The proposed motion planner is implemented and tested both in simulation and on a real autonomous vehicle in three different scenarios. Experiments show that the planner outputs high-quality trajectories and performs intelligent driving behaviors.

A prediction- and cost function-based algorithm for robust autonomous freeway driving

In this paper, a prediction- and cost function-based algorithm (PCB) is proposed to implement robust freeway driving in autonomous vehicles. A prediction engine is built to predict the future microscopic traffic scenarios. With the help of a human-understandable and representative cost function library, the predicted traffic scenarios are evaluated and the best control strategy is selected based on the lowest cost. The prediction- and cost function-based algorithm is verified using the simulator of the autonomous vehicle Boss from the DARPA Urban Challenge 2007. The results of both case tests and statistical tests using PCB show enhanced performance of the autonomous vehicle in performing distance keeping, lane selecting and merging on freeways.

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.

Motion planning under uncertainty for on-road autonomous driving

We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous vehicle, the uncertainty from localization and control is estimated based on a Linear-Quadratic Gaussian (LQG) framework. Compared with other safety assessment methods, our framework allows the planner to avoid unsafe situations more efficiently, thanks to the direct uncertainty information feedback to the planner. We also demonstrate our planners ability to generate safer trajectories compared to planning only with a LQG framework.

A multi-level collaborative driving framework for autonomous vehicles

This paper proposes a multi-level collaborative driving framework (MCDF) for human-autonomous vehicle interaction. There are three components in MCDF; the mission-behavior-motion block diagram, the functionality-module relationship and the human participation level table. Through integration of the three components, a human driver can cooperate with the vehicles intelligence to achieve better driving performance, robustness and safety. The MCDF is successfully implemented in TROCS, a real-time autonomous vehicle control system developed by the Tartan Racing Team for the 2007 DARPA Urban Challenge. The performance of MCDF is analyzed and a preliminary test in TROCS simulation mode shows that MCDF is effective in improving the driving performance.

A learning-based autonomous driver: emulate human driver's intelligence in low-speed car following

In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human drivers decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (< 30miles/h) car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like carfollowing after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.

Context-aware tracking of moving objects for distance keeping

We propose a robust object tracking algorithm for distance keeping. Taking advantage of a context-based region of interest, we are able to maximize the performance of each sensor, and reduce the computation time since we only focus on the targets inside the region. Tracking targets in road coordinates enables finding the distance-keeping target on any curved road, while a commercial Adaptive Cruise Control (ACC) system works best on straight roads. We demonstrate that the overall performance of the proposed algorithm is better than that of a commercial ACC system. The distance-keeping target can either be used for lane following for a standalone ACC system or an autonomous vehicle. Our object tracking algorithm can also be extended to find the target of interest for lane changing or ramp merging for an autonomous vehicle.