Driving Behavior Group

Mission

Driving behavior group is studying data-oriented modeling approaches to the human driving behavior based on signal processing and machine learning of multi-modal sequential data. The topics of this sub group so far covered; driver recognition, driving behavior prediction, risky driving detection and so on. We are also actively building unique signal corpora such as NU-Drive and 1000-lane change.

運転行動グループは、大規模な系列データの信号処理・機械学習に基づく方法論で、自動車の運転をモデル化する研究に取り組んでいます。研究グループはこれまで、運転者の認識、運転行動の予測、危険な運転の検出といった研究を行ってきました。グループでは大規模な運転行動データベース(NU-Driveや1000-lane change)の構築・提供にも力を入れています

Projects

Detecting Risky Lane Changes Using Ego and Surrounding Vehicle Information Integrated by a Multi-modal Variational Autoencoder

マルチモーダル変分自己符号化器を用いた自車挙動・周辺車挙動情報の統合による危険車線変更の検出

This project focuses on detecting risky lane changes by integrating ego and surrounding vehicle information using a multi-modal variational autoencoder (MVAE). The relationship between ego and surrounding vehicle behavior during safe (or normal) driving is represented using the MVAE, under the assumption that the reconstruction error, i.e., the difference between the input and output signals, will be relatively larger for risky (or unusual) driving situations compared to those for safe (or normal) driving situations. We conduct a risky lane change detection experiment using expressway driving data, which includes 432 lane changes to the right executed by ten drivers during passing maneuvers. The ground truth for the level of risk for each lane change is based on the subjective evaluations of ten human evaluators.

We plan to explore the use of a recurrent structure for the autoencoders, which would allow the use of temporal context within a time series of driving data for behavior detection.


運転の危険シーンを深層学習の一つである自己符号化器(オートエンコーダ)を用いて検出する手法を提案する.オートエンコーダを安全運転のデータで学習することで,危険シーンが入力された場合の入力信号と出力信号の差,つまり,信号の「再構成誤差」が大きくなると考え,この再構成誤差の大きさで危険か否かを判断する.また,入力情報として,自車挙動と周辺車挙動の関係をマルチモーダルの変分オートエンコーダで統合する.提案手法により,自車・周辺車独立のオートエンコーダを統合したモデルに比較し,危険運転の検出率が向上することを確認した.

Personalized Safety-focused Control by Minimizing Subjective Risk

主観的リスクの最小化に基づく個人適応型車両安全制御

Objective driving risk assessment has a long history and mature methodology background, however, research field of subjective risk assessment, causing reason understanding and risk factor identification are still remaining blank. We built subjective risk model (1) to analyze individual differences of subjective risk causing factors from ego vehicle driving signals and surrounding vehicle signals, and (2) to build a subjective risk assessment model to detect risky or non-risky lane change scenes.

In addition, we work on a data-driven control framework for autonomous driving which combines learning-based risk assessment with personalized, safety-focused, predictive control. Different control strategies are employed depending on driving situation (risky vs. non-risky).

Contributions:

  1. A data-driven framework to model subjective risk for individuals during lane change scenarios.
  2. Driving features including ego vehicle driving signals and surrounding vehicle directions, to understand individual differences on perceiving driving risk.
  3. Exploring effective factors that affect driving risk can quantify the safety of individual driving behavior and use it as an indicator of accident risk to achieve effective risk prediction for individuals.
  4. SMPC: Safety-focused Model Predictive Control for constraining distances to surrounding vehicles
  5. PMPC: Personalized Model Predictive Control for tracking personalized velocity


人が運転に対して感じる危険のレベルや要因は人それぞれである.そこでドライバあるいは同乗者の危険の感じ方や個人性を考慮した運転支援や自動運転制御を行う手法について検討する.まず,ドライバの感じる危険感とその要因を抽出する.その結果に基づき,個人の好みに応じて周辺車との位置関係や速度等を制御する.

Driving Feature Extraction to Reproduce Driving Styles for Autonomous Vehicles

人の運転スタイルの再現に基づく自動運転AIエージェントの構築

We work towards creating an AI driving agent by extracting behaviors from human expert drivers’ data. Deep learning techniques were used to extract latent features to create velocity profiles so that an autonomous driving agent could drive in a human-like manner. Using a method to compare trajectories, it was shown that the agent was able to use the extracted proactive behaviors to drive similarly to a skilled human driver.

Contributions:

  1. A data-driven approach with deep auto-encoders to extract driving features and cluster driving behaviors. The behaviors are then used in a driving agent which can drive similarly to a human driver.
  2. We clustered these latent features into behaviors and created velocity profiles. This allowed us to create an autonomous agent to correctly select the proper behavior to use depending on the environment it was in.

Future works stemming from this work will focus on dynamic obstacles. We would like to use our techniques to extract behaviors and create driving agents that are able to navigate while avoiding dynamic obstacles such as traffic and pedestrians.

The end-goal of this technique would be to generate required driving behaviors for an autonomous agent simply by giving it a path and a vector map. This can likely be done with extensive data to train more types of behaviors and a recurrent neural network (RNN) such as a long term-short term memory (LSTM) network to identify the required behaviors along the path.


人の運転を再現することで自動運転を行うAIエージェントを構築する.まずオートエンコーダを用いて記録された運転データに含まれる潜在的な特徴を抽出する.これらの運転行動の潜在特徴をクラスタリングし,その結果に基づいて人の運転行動を模擬したドライビングエージェントを構築する.