Conditional Latent ODEs for Motion Prediction in Autonomous Driving

This paper addresses imitation learning for motion prediction problem in autonomous driving, especially in multi-agent setting. Different from previous methods based on GAN, we present the conditional latent ordinary differential equation (cLODE) to leverage both the generative strength of conditional VAE and the continuous representation of neural ODE. Our network architecture is inspired from the Latent-ODE model. The experiment shows that our method outperform the baseline methods in the simulation of multi-agent driving and is very efficient in term of GPU memory consumption. Our code and docker image are publicly available: https://github.com/TruongKhang/cLODE; https://hub.docker.com/r/kim4375731/clode.

An Autonomous System for Head-to-Head Race: Design, Implementation and Analysis; Team KAIST at the Indy Autonomous Challenge

While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is a new area of research that has been attracting considerable interest recently. Due to the fact that a vehicle is driven by its perception, planning, and control limits during racing, numerous research and development issues arise. This paper provides a comprehensive overview of the autonomous racing system built by team KAIST for the Indy Autonomous Challenge (IAC). Our autonomy stack consists primarily of a multi-modal perception module, a high-speed overtaking planner, a resilient control stack, and a system status manager. We present the details of all components of our autonomy solution, including algorithms, implementation, and unit test results. In addition, this paper outlines the design principles and the results of a systematical analysis. Even though our design principles are derived from the unique application domain of autonomous racing, they can also be applied to a variety of safety-critical, high-cost-of-failure robotics applications. The proposed system was integrated into a full-scale autonomous race car (Dallara AV-21) and field-tested extensively. As a result, team KAIST was one of three teams who qualified and participated in the official IAC race events without any accidents. Our proposed autonomous system successfully completed all missions, including overtaking at speeds of around $220 km/h$ in the IAC@CES2022, the world's first autonomous 1:1 head-to-head race.

Resilient Navigation and Path Planning System for High-speed Autonomous Race Car

This paper describes resilient navigation and planning algorithm for high-speed autonomous race, Indy Autonomous Challenge (IAC). The IAC is a competition with full-scale autonomous race car that can drive up to 290 km/h(180mph). Due to its high-speed and heavy vibration of the race car, GPS/INS system is prone to be degraded. These degraded GPS measurements can cause a critical localization error leading to a serious crashing accidents. To this end, we propose a robust navigation system to implement multi-sensor fusion Kalman filter. In this study, we present how to identify the degradation of measurement based on probabilistic approaches. Based on this approach, we could compute optimal measurement values for Kalman filter correction step. At the same time, we present the other resilient navigation system so that race car can follow the race track in fatal localization failure situations. In addition, this paper also covers the optimal path planning algorithm for obstacle avoidance. To take account for original optimal racing line, obstacles, vehicle dynamics, we propose a road-graph-based path planning algorithm to guarantee our race car drives in-bounded conditions. In the experiments, we will evaluate our designed localization system can handle the degraded data, and sometimes prevent serious crashing accidents while high-speed driving. In addition, we will describe how we successfully completed the obstacle avoidance challenge.

Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racing

Resolving edge-cases in autonomous driving, head-to-head autonomous racing is getting a lot of attention from the industry and academia. In this study, we propose a game-theoretic model predictive control (MPC) approach for head-to-head autonomous racing and data-driven model identification method. For the practical estimation of nonlinear model parameters, we adopted the hyperband algorithm, which is used for neural model training in machine learning. The proposed controller comprises three modules: 1) game-based opponents' trajectory predictor, 2) high-level race strategy planner, and 3) MPC-based low-level controller. The game-based predictor was designed to predict the future trajectories of competitors. Based on the prediction results, the high-level race strategy planner plans several behaviors to respond to various race circumstances. Finally, the MPC-based controller computes the optimal control commands to follow the trajectories. The proposed approach was validated under various racing circumstances in an official simulator of the Indy Autonomous Challenge. The experimental results show that the proposed method can effectively overtake competitors, while driving through the track as quickly as possible without collisions.