Search for: All records

The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approaches that are used in the fields of perception, planning and control as well as end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to a range of high performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms emphasizing both the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field we conclude with a summary of open research challenges that will guide future researchers in this field. 

This paper presents an adaptive lookahead pure-pursuit lateral controller for optimizing racing metrics such as lap time, average lap speed, and deviation from a reference trajectory in an autonomous racing scenario. We propose a greedy algorithm to compute and assign optimal lookahead distances for the pure-pursuit controller for each waypoint on a reference trajectory for improving the race metrics. We use a ROS based autonomous racing simulator to evaluate the adaptive pure-pursuit algorithm and compare our method with several other pure-pursuit based lateral controllers. We also demonstrate our approach on a scaled real testbed using a F1/10 autonomous racecar. Our method results in a significant improvement (20%) in the racing metrics for an autonomous racecar. 

Multi-agent autonomous racing is a challenging problem for autonomous vehicles due to the split-second, and complex decisions that vehicles must continuously make during a race. The presence of other agents on the track requires continuous monitoring of the ego vehicle’s surroundings, and necessitates predicting the behavior of other vehicles so the ego can quickly react to a changing environment with informed decisions. In our previous work we have developed the DeepRacing AI framework for autonomous formula one racing. Our DeepRacing framework was the first implementation to use the highly photorealisitc Formula One game as a simulation testbed for autonomous racing. We have successfully demonstrated single agent high speed autonomous racing using Bezier curve trajectories. In this paper, we extend the capabilities of the DeepRacing framework towards multi-agent autonomous racing. To do so, we first develop and learn a virtual camera model from game data that the user can configure to emulate the presence of a camera sensor on the vehicle. Next we propose and train a deep recurrent neural network that can predict the future poses of opponent agents in the field of view of the virtual camera using vehicles position, velocity, and heading data with respect to the ego vehicle racecar. We demonstrate early promising results for both these contributions in the game. These added features will extend the DeepRacing framework to become more suitable for multi-agent autonomous racing algorithm development 

A popular metric for measuring progress in autonomous driving has been the "miles per intervention". This is nowhere near a sufficient metric and it does not allow for a fair comparison between the capabilities of two autonomous vehicles (AVs). In this paper we propose Scenario2Vector - a Scenario Description Language (SDL) based embedding for traffic situations that allows us to automatically search for similar traffic situations from large AV data-sets. Our SDL embedding distills a traffic situation experienced by an AV into its canonical components - actors, actions, and the traffic scene. We can then use this embedding to evaluate similarity of different traffic situations in vector space. We have also created a first of its kind, Traffic Scenario Similarity (TSS) dataset which contains human ranking annotations for the similarity between traffic scenarios. Using the TSS data, we compare our SDL embedding -with textual caption based search methods such as Sentence2Vector. We find that Scenario2Vector outperforms Sentence2Vector by 13% ; and is a promising step towards enabling fair comparisons among AVs by inspecting how they perform in similar traffic situations. We hope that Scenario2Vector can have a similar impact to the AV community that Word2Vec/Sent2Vec have had in Natural Language Processing datasets.