Evaluation of automated vehicles encountering pedestrians at unsignalized crossings

Chen, Baiming; Zhao, Ding; Peng, Huei

doi:10.1109/ivs.2017.7995950

Cited by 33 publications

(27 citation statements)

References 11 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…during encounters at unsignalized crossing (Chen et al 2017), to predict when a pedestrian will cross (Völz et al 2016), or to categorizing danger in vehiclepedestrian encounters (Otsuka et al 2017). Similar studies are also being performed for cyclists.…”

Section: Context Cues For Vru Behaviorsmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

View full text Add to dashboard Cite

Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide context on the target's behavior, and incorporate these in a Dynamic Bayesian Network (DBN). The DBN extends the SLDS by conditioning the mode transition probabilities on additional context states. We describe efficient online inference in this DBN for probabilistic path prediction, accounting for uncertainty in both measurements and target behavior. Our approach is illustrated on two scenarios in the Intelligent Vehicles domain concerning pedestrians and cyclists, so-called Vulnerable Road Users (VRUs). Here, context cues include the static environment of the VRU, its dynamic environment, and its observed actions. Experiments using stereo vision data from a moving vehicle demonstrate that the proposed approach results in more accurate path prediction than SLDS at the relevant short time horizon (1 s). It slightly outperforms a computationally more demanding state-of-the-art method.

show abstract

Section: Context Cues For Vru Behaviorsmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Multiple metrics are available to evaluate performance [9], [19]. We calculate the mean velocity v, the mean acceleration a, the mean collision velocity dv, the number of collisions, and the amount of emergency brake interventions, over all of the EuroNCAP scenarios.…”

Section: A Evaluation Metricmentioning

confidence: 99%

“…Previous work on evaluating autonomous braking systems at unsignalized crosswalks relied on datadriven models of the pedestrian [9]. Chen, Zhao, and Peng argue that the EuroNCAP scenarios would allow an overly conservative system to be validated [9]. To avoid such an issue, we augmented the suite of scenarios with a situation involving occluded areas at the side of the road with no pedestrian crossing.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Collision Avoidance System for Scenarios with Occlusions

Schraner

Bouton

Kochenderfer

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. In this work, we formulate the problem as a partially observable Markov decision process (POMDP), to derive a policy robust to uncertainty in the pedestrian location. We investigate how to integrate such a policy with an AEB system that operates only when a collision is unavoidable. In addition, we propose a rigorous evaluation methodology on a set of well defined scenarios. We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.

show abstract

“…A more complex modeling of pedestrian behavior is left as future work. Previous work presents data driven approaches for modeling pedestrian behavior at crosswalk [6]. The agent receives a reward when reaching a terminal state.…”

Section: Model Descriptionmentioning

confidence: 99%

Decomposition methods with deep corrections for reinforcement learning

Bouton

Julian

Nakhaei

et al. 2019

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently. An arbitrator is then responsible for combining the individual utilities and selecting an action in real time to solve the global problem. Although these techniques can perform well empirically, they rely on strong assumptions of independence between the local tasks and sacrifice the optimality of the global solution. This paper proposes an approach that improves upon such approximate solutions by learning a correction term represented by a neural network. We demonstrate this approach on a fisheries management problem where multiple boats must coordinate to maximize their catch over time as well as on a pedestrian avoidance problem for autonomous driving. In each problem, decomposition methods can scale to multiple boats or pedestrians by using strategies involving one entity. We verify empirically that the proposed correction method significantly improves the decomposition method and outperforms a policy trained on the full scale problem without utility decomposition. decomposition methods with deep corrections for reinforcement learning 2 bined in real-time through an arbitrator that maximizes the expected utility [22]. Applications to aircraft collision avoidance with multiple intruders explore summing or using the minimum state-action values [7], [20]. While these approaches tend to perform well empirically, they sacrifice optimality. The solution to each subproblem assumes that its individual policy will be followed regardless of other entities, in contrast to the global policy considering all subproblems [23].Previous approaches to scale decision algorithms using decomposition methods relied on a distributed agent architecture [22]. Each agent is responsible for addressing one of the multiple objectives required to achieve a complex task. At each time step an arbitrator must decide between the different actions recommended by these agents and address possible conflicts. Possible arbitration strategies are command fusion [7], voting [22], lexicographic ordering [33] or utility fusion [22], [23]. It has been shown that utility fusion offers a more principled way of deciding between the individual agents compared to a voting-based approach or command fusion [22]. An alternative approach in leader follower scenarios consists of reducing the problem of controlling the group of agent to controlling a single group leader [15]. Other approaches rely on distributed learning algorithms, such as independent Q-learning, where each agent learns a policy without being aware of the other agents actions [28]. The underlying assumption of independence between agents in these algorithms trades off the benefit of collaboration for an easier learning of the task [8].Utility decomposition methods have been used in many practic...

show abstract

Evaluation of automated vehicles encountering pedestrians at unsignalized crossings

Cited by 33 publications

References 11 publications

Context-Based Path Prediction for Targets with Switching Dynamics

Context-Based Path Prediction for Targets with Switching Dynamics

Pedestrian Collision Avoidance System for Scenarios with Occlusions

Decomposition methods with deep corrections for reinforcement learning

Contact Info

Product

Resources

About